From 283ea6abc995d340d3a2d438f125b22612c34e31 Mon Sep 17 00:00:00 2001
From: Ian Limarta <ian.limarta@gmail.com>
Date: Tue, 10 Dec 2024 12:27:39 -0600
Subject: [PATCH 1/8] feat: generative model

---
 src/genjaxmix/__init__.py                |   5 +
 src/genjaxmix/conjugacy.py               |  20 ++
 src/genjaxmix/dpmm.py                    | 103 ++++++++++
 src/genjaxmix/jax_distributions.py       | 128 +++++++++++++
 src/genjaxmix/rejuvenation.py            |  98 ++++++++++
 src/genjaxmix/smc.py                     | 232 +++++++++++++++++++++++
 src/genjaxmix/utils.py                   |  19 ++
 src/genjaxmix/vectorized.py              |  43 +++++
 src/genjaxmix/vectorized_rejuvenation.py | 119 ++++++++++++
 9 files changed, 767 insertions(+)
 create mode 100644 src/genjaxmix/__init__.py
 create mode 100644 src/genjaxmix/conjugacy.py
 create mode 100644 src/genjaxmix/dpmm.py
 create mode 100644 src/genjaxmix/jax_distributions.py
 create mode 100644 src/genjaxmix/rejuvenation.py
 create mode 100644 src/genjaxmix/smc.py
 create mode 100644 src/genjaxmix/utils.py
 create mode 100644 src/genjaxmix/vectorized.py
 create mode 100644 src/genjaxmix/vectorized_rejuvenation.py

diff --git a/src/genjaxmix/__init__.py b/src/genjaxmix/__init__.py
new file mode 100644
index 0000000..376bc60
--- /dev/null
+++ b/src/genjaxmix/__init__.py
@@ -0,0 +1,5 @@
+from .dpmm import generate
+
+__all__ = [
+    "generate"
+]
\ No newline at end of file
diff --git a/src/genjaxmix/conjugacy.py b/src/genjaxmix/conjugacy.py
new file mode 100644
index 0000000..c124f6e
--- /dev/null
+++ b/src/genjaxmix/conjugacy.py
@@ -0,0 +1,20 @@
+import jax.numpy as jnp
+import jax
+from .dpmm import K, L_num
+
+def posterior_normal_inverse_gamma(assignments, x, mu_0=0.0, v_0=1.0, a_0=1.0, b_0=1.0):
+    counts = jnp.bincount(assignments, length=K)
+    sum_x = jax.ops.segment_sum(x, assignments, K)
+    sum_x_sq = jax.ops.segment_sum(x**2, assignments, K)
+
+    v_n_inv = 1/v_0 + counts
+    m = (1/v_0 * mu_0 + sum_x) / v_n_inv
+    a = a_0 + counts / 2
+    b = b_0 + 0.5 * (sum_x_sq + 1/v_0*mu_0**2 - v_n_inv * m ** 2)
+    return m, 1/v_n_inv, a, b
+
+def posterior_dirichlet(assignments, x):
+    one_hot_c = jax.nn.one_hot(assignments, K)
+    one_hot_y = jax.nn.one_hot(x, L_num)
+    frequency_matrix = one_hot_c.T @ one_hot_y
+    return frequency_matrix
\ No newline at end of file
diff --git a/src/genjaxmix/dpmm.py b/src/genjaxmix/dpmm.py
new file mode 100644
index 0000000..e59b6c2
--- /dev/null
+++ b/src/genjaxmix/dpmm.py
@@ -0,0 +1,103 @@
+from genjax import gen, repeat
+from genjax import normal, inverse_gamma, dirichlet, categorical, beta
+import jax.numpy as jnp
+from .utils import beta_to_logpi
+
+K = 5 
+L_num = 7
+
+@gen
+def hyperparameters(mu_0=0.0, l=1.0, shape=1.0, scale=1.0, alpha=1.0):
+    """
+        µ, sigma^2 ~ N(µ|m_0, sigma^2*l)IG(sigma^2| shape, scale)
+    """
+    sigma_sq = inverse_gamma(shape*jnp.ones(K), scale*jnp.ones(K)) @ "sigma"
+    sigma = jnp.sqrt(sigma_sq)
+    mu = normal(mu_0 * jnp.ones(K), sigma * l) @ "mu"
+    logp = dirichlet(alpha*jnp.ones((K, L_num))) @ "logp"
+    logp = jnp.log(logp)
+    return mu, sigma, logp
+
+@gen
+def cluster(pi, mu, sigma, logp):
+    """Sample from a mixture model with proportions ``pi``, normal inverse gamma parameters ``mu`` and ``sigma``, and 
+    categorical parameter ``logp``.
+
+    Args:
+        pi:
+        mu:
+        sigma:
+        logp:
+    
+    Returns:
+        idx:
+        y1:
+        y2:
+
+    """
+    idx = categorical(pi) @ "c"
+    y1 = normal(mu[idx], sigma[idx]) @ "y1"
+    y2 = categorical(logp[idx]) @ "y2"
+    return idx, y1, y2
+
+@gen
+def gem(alpha):
+    """Sample from a Griffiths, Engen, and McCloskey's (GEM) distribution with concentration ``alpha``.
+
+    Args:
+        alpha: a positive scalar
+    
+    Returns:
+        A random array given by shape ``K``
+        
+    """
+    betas = beta(jnp.ones(K), alpha*jnp.ones(K)) @ "pi"
+    pi = beta_to_logpi(betas)
+    return pi
+
+
+# @gen
+# def dpmm(concentration=1.0, mu_0=0.0, l=1.0, a=1.0, b=1.0):
+#     """Sample from a Dirichlet process mixture model.
+
+#     Args:
+#         concentration: 
+#         mu_0: ?
+#         precision: ?
+#         a: shape of the inverse gamma
+#         b: scale of the inverse gamma
+    
+#     Returns:
+#         A triplet ``(c, y1, y2)`` of three arrays. The first value, ``c``, is the assignments. The values ``y1` and ``y2``
+#         represent the numerical and categorical features of each data point, respectively.
+#     """
+
+#     logpi = gem(concentration) @ "pi"
+#     mu, sigma, logp = hyperparameters(mu_0, l, a, b) @ "hyperparameters"
+#     y = cluster_repeat(logpi, mu, sigma, logp) @ "assignments"
+#     return y
+
+def generate(N_max):
+    """Sample from a Dirichlet process mixture model.
+
+    Args:
+        concentration: 
+        mu_0: ?
+        precision: ?
+        a: shape of the inverse gamma
+        b: scale of the inverse gamma
+    
+    Returns:
+        A triplet ``(c, y1, y2)`` of three arrays. The first value, ``c``, is the assignments. The values ``y1` and ``y2``
+        represent the numerical and categorical features of each data point, respectively.
+    """
+    cluster_repeat = repeat(n=N_max)(cluster)
+
+    @gen
+    def dpmm(concentration=1.0, mu_0=0.0, l=1.0, a=1.0, b=1.0):
+
+        logpi = gem(concentration) @ "pi"
+        mu, sigma, logp = hyperparameters(mu_0, l, a, b) @ "hyperparameters"
+        y = cluster_repeat(logpi, mu, sigma, logp) @ "assignments"
+        return y
+    return dpmm
\ No newline at end of file
diff --git a/src/genjaxmix/jax_distributions.py b/src/genjaxmix/jax_distributions.py
new file mode 100644
index 0000000..9f106f9
--- /dev/null
+++ b/src/genjaxmix/jax_distributions.py
@@ -0,0 +1,128 @@
+import jax
+import jax.numpy as jnp
+from tensorflow_probability.substrates import jax as tfp
+
+import genjax
+from genjax import Pytree
+from genjax._src.generative_functions.distributions.distribution import Distribution
+from genjax.typing import PRNGKey, Array, Float
+
+tfd = tfp.distributions
+
+@Pytree.dataclass
+class NormalInverseGamma(Distribution):
+    def random_weighted(self, key: PRNGKey, mu, l, a, b):
+        ig = tfd.InverseGamma(concentration=a, scale=b)
+        key, subkey = jax.random.split(key)
+        precision = ig.sample(seed=subkey)
+        ig_logp = ig.log_prob(precision)
+
+        normal = tfd.Normal(loc=mu, scale=precision / l)
+        key, subkey = jax.random.split(key)
+        mu = normal.sample(seed=subkey)
+        mu_logp = normal.log_prob(mu)
+        
+        retval = jnp.stack([mu, precision], axis=1)
+        inv_logp = -jnp.sum(ig_logp) - jnp.sum(mu_logp)
+        return inv_logp, retval
+
+    def estimate_logpdf(self, key: PRNGKey, x, mu, l, a, b):
+        mu_sampled = x[:,0]
+        precision = x[:,1]
+        ig = tfd.InverseGamma(concentration=a, scale=b)
+        ig_logp = ig.log_prob(precision)
+        normal = tfd.Normal(loc=mu, scale= precision/l)
+        mu_logp = normal.log_prob(mu_sampled)
+        return jnp.sum(ig_logp) + jnp.sum(mu_logp)
+
+@Pytree.dataclass
+class Dirichlet(Distribution):
+    def random_weighted(self, key:PRNGKey, alpha):
+        dir = tfd.Dirichlet(concentration = alpha)
+        probs = dir.sample(seed=key)
+        inv_weight = -dir.log_prob(probs)
+        return inv_weight, probs
+    def estimate_logpdf(self, key:PRNGKey, x, alpha):
+        dir = tfd.Dirichlet(concentration = alpha)
+        return dir.log_prob(x)
+
+"""
+A class to store DP samples and the corresponding beta values. 
+
+Used in GEM to avoid floating point error
+"""
+@Pytree.dataclass
+class DPSample(Pytree):
+    betas: Array
+    pi: Array
+    def __init__(self, betas, pi):
+        self.betas = betas
+        self.pi = pi
+
+@Pytree.dataclass
+class GEM(Distribution):
+    C: int = Pytree.static(default=1)
+    def __init__(self, C:int=10):
+        self.C = jnp.asarray(C)
+    def random_weighted(self, key: PRNGKey, alpha: Float):
+        C = self.C
+        sampler = tfd.Beta(concentration1 = jnp.array(alpha), concentration0=jnp.array(1.0))
+        betas = sampler.sample(seed=key, sample_shape = C)
+        inv_weight = -jnp.sum(sampler.log_prob(betas))
+        betas_not = 1-betas
+
+        betas = jnp.log(betas)
+        betas_not = jnp.log(betas_not)
+        # prefix sum of betas
+        logpi = jnp.zeros(C)
+        for i in range(1,C):
+            logpi = logpi.at[i].set(jnp.sum(betas_not[:i]))
+        for i in range(C):
+            logpi = logpi.at[i].set(logpi[i] + betas[i])
+
+        return inv_weight, logpi
+
+    def estimate_logpdf(self, key: PRNGKey, pi, alpha: Float):
+        # assumes dist.pi corresponds to dist.betas
+        sampler = tfd.Beta(concentration1 = jnp.array(alpha), concentration0 = jnp.array(1.0))
+        def unfold(carry, pi):
+            logbeta = pi - carry
+            return carry + jnp.log(-jnp.expm1(logbeta)) , jnp.exp(logbeta)
+
+        _, betas = jax.lax.scan(unfold, 0.0, pi)
+        weight = jnp.sum(sampler.log_prob(betas))
+        return weight
+
+nig = NormalInverseGamma()
+dirichlet = Dirichlet()
+@Pytree.dataclass
+class MixtureModel(Distribution):
+    def random_weighted(self, key, pi, categorical_probs):
+        key_0, key_1 = jax.random.split(key, 2)
+        cluster_dist = tfd.Categorical(pi)
+        c = cluster_dist.sample(seed=key_0)
+        c_logp = cluster_dist.log_prob(c)
+        label_dist = tfd.Categorical(categorical_probs[c])
+        y = label_dist.sample(seed=key_1)
+        y_logp = label_dist.log_prob(y)
+        return -c_logp-y_logp, (c,y)
+
+    def estimate_logpdf(self, x, pi, categorical_probs):
+        c, y = x
+        cluster_dist = tfd.Categorical(pi)
+        label_dist = tfd.Categorical(categorical_probs[c])
+        logp = cluster_dist.log_prob(c) + label_dist.log_prob(y)
+        return -logp
+
+cmm = MixtureModel()
+
+@genjax.repeat(n=100)
+@gen 
+def cluster(pi, probs):
+    assignments = cmm(pi, probs) @ "assignments"
+    return assignments
+
+pi = jnp.ones(10) / 10
+categorical_probs = jax.random.uniform(key, (10, 36, 19))
+tr = cluster.simulate(key, (pi, categorical_probs,))
+tr.get_choices()[0, "assignments"].unmask()
\ No newline at end of file
diff --git a/src/genjaxmix/rejuvenation.py b/src/genjaxmix/rejuvenation.py
new file mode 100644
index 0000000..522f571
--- /dev/null
+++ b/src/genjaxmix/rejuvenation.py
@@ -0,0 +1,98 @@
+import jax
+import jax.numpy as jnp
+from genjax import inverse_gamma, normal, dirichlet, Pytree,  gen
+from genjax.typing import PRNGKey
+from genjax._src.generative_functions.distributions.distribution import Distribution
+from tensorflow_probability.substrates import jax as tfp
+from genjax._src.core.interpreters.incremental import Diff
+from .dpmm import K
+from .utils import beta_to_logpi, logpi_to_beta
+from .conjugacy import posterior_dirichlet, posterior_normal_inverse_gamma
+
+tfd = tfp.distributions
+
+def gibbs_move(model, proposal, model_args, tr, observations, key):
+    proposal_args = (observations,)
+    fwd_choices, fwd_weight, _ = proposal.propose(key, proposal_args)
+
+    key, subkey = jax.random.split(key)
+    argdiffs = Diff.no_change(model_args)
+    tr_new, weight, _, discard = model.update(subkey, tr, fwd_choices, argdiffs)
+    return tr_new
+
+
+@gen
+def propose_parameters(obs):
+    _propose_parameters(obs) @ "hyperparameters"
+
+@gen
+def _propose_parameters(obs):
+    c = obs[:, "c"]
+    y1 = obs[:, "y1"]
+    y2 = obs[:, "y2"]
+
+    mu_n, v_n, a, b = posterior_normal_inverse_gamma(c, y1)
+    frequency_matrix = posterior_dirichlet(c, y2) + 1e-6 # to prevent degeneracy
+
+    # Propose sigma
+    sigma_sq = inverse_gamma(a,b) @ "sigma"
+    sigma = jnp.sqrt(sigma_sq)
+
+    # Propose mu
+    mu = normal(mu_n, sigma*v_n) @ "mu"
+
+    # Propose logp
+    p = dirichlet(frequency_matrix) @ "logp"
+    
+    return mu, sigma, p
+
+
+def apply_decay(x, gamma):
+    decay_factors = jnp.arange(x.shape[0]) * jnp.log(gamma)
+    logpi = jnp.log(x) + decay_factors 
+    log_max = jnp.max(logpi)
+    log_shifted = logpi - log_max
+    
+    # Compute log-sum-exp for normalization
+    # Normalize in log-space
+    log_norm = jnp.log(jnp.sum(jnp.exp(log_shifted)))
+    logpi = log_shifted - log_norm
+    return logpi
+
+
+@Pytree.dataclass
+class DirichletBeta(Distribution):
+    def random_weighted(self, key: PRNGKey, alpha):
+        sampler = tfd.Dirichlet(concentration = alpha)
+        pi = sampler.sample(seed=key)
+        logpi = jnp.log(pi)
+        # logpi = apply_decay(pi, gamma=0.80)
+
+        betas = logpi_to_beta(logpi)
+
+        inv_weight = -sampler.log_prob(pi)
+
+        return inv_weight, betas
+
+    def estimate_logpdf(self, key: PRNGKey, betas, alpha):
+        sampler = tfd.Dirichlet(concentration = alpha)
+
+        logpi = beta_to_logpi(betas)
+        pi = jnp.exp(logpi)
+
+        weight = jnp.sum(sampler.log_prob(pi))
+        return weight
+
+dirichlet_beta = DirichletBeta()
+
+@gen 
+def propose_pi(obs):
+    pi = _propose_pi(obs) @ "pi"
+    return pi
+
+@gen
+def _propose_pi(obs):
+    c = obs[:, "c"]
+    proportions = jnp.bincount(c, length = K) + 1e-6
+    pi = dirichlet_beta(proportions) @ "pi"
+    return pi
\ No newline at end of file
diff --git a/src/genjaxmix/smc.py b/src/genjaxmix/smc.py
new file mode 100644
index 0000000..adeaf19
--- /dev/null
+++ b/src/genjaxmix/smc.py
@@ -0,0 +1,232 @@
+import jax.numpy as jnp
+import jax
+import numpy as np
+from plum import dispatch
+from jaxtyping import Array, Integer
+from genjaxmix.distributions import (Dirichlet, MixtureModel,
+    posterior, sample, logpdf, Normal, Categorical, Mixed, Cluster, Trace)
+from functools import partial
+
+@partial(jax.jit, static_argnames=['gibbs_iters', 'max_clusters', 'n_steps'])
+def smc(key, trace, data_test, n_steps, data, gibbs_iters, max_clusters):
+    smc_keys = jax.random.split(key, n_steps)
+
+    def wrap_step(trace, n):
+        key = smc_keys[n]
+        keys = jax.random.split(key, 3)
+        new_cluster = step(data=data, trace=trace, gibbs_iters=gibbs_iters, 
+            max_clusters=max_clusters, key=keys[0], K=n+2)
+        split_trace = Trace(
+            gem=trace.gem,
+            g=trace.g,
+            cluster=new_cluster
+        )
+
+        rejuvenated_cluster = rejuvenate(keys[1], data, split_trace, gibbs_iters, max_clusters)
+        rejuvenated_trace = Trace(
+            gem=split_trace.gem,
+            g=trace.g,
+            cluster=rejuvenated_cluster
+        )
+
+        mixture_model = MixtureModel(
+            pi=rejuvenated_trace.cluster.pi/jnp.sum(rejuvenated_trace.cluster.pi), 
+            f=rejuvenated_trace.cluster.f[:max_clusters])
+        logprobs = jax.vmap(logpdf, in_axes=(None, 0))(mixture_model, data_test)
+        sum_logprobs = jnp.sum(logprobs)
+
+        return rejuvenated_trace, (rejuvenated_trace, sum_logprobs)
+
+    carry, (trace, sum_logprobs) = jax.lax.scan(wrap_step, trace, jnp.arange(n_steps))
+    return trace, sum_logprobs
+
+
+
+def rejuvenate(key, data, trace, gibbs_iters, max_clusters):
+    extended_pi = jnp.concatenate((trace.cluster.pi, jnp.zeros(max_clusters)))
+    log_likelihood_mask = jnp.where(extended_pi == 0, -jnp.inf, 0)
+
+    partial_gibbs_step = partial(gibbs_step, 
+        alpha=trace.gem.alpha, g=trace.g, data=data, 
+        log_likelihood_mask=log_likelihood_mask, max_clusters=max_clusters,
+        rejuvenation=True)
+
+    keys = jax.random.split(key, gibbs_iters)
+    _, q_split_trace = jax.lax.scan(partial_gibbs_step, trace.cluster.c, keys)
+
+    cluster = q_split_trace[-1]
+    cluster = Cluster(cluster.c, jnp.sum(trace.cluster.pi) * cluster.pi[:max_clusters], cluster.f)
+
+    return cluster
+
+@partial(jax.jit, static_argnames=['gibbs_iters', 'max_clusters'])
+def step(data, gibbs_iters, key, K, trace, max_clusters):
+    q_split_trace = q_split(data, gibbs_iters, max_clusters, key, trace.cluster.c, trace.gem.alpha, trace.g)
+
+    cluster_weights = get_weights(trace, K, data, q_split_trace, max_clusters)
+
+    logprob_pi0 = logpdf(trace.gem, jnp.sort(trace.cluster.pi, descending=True), K-1)
+
+    weights = jnp.zeros(max_clusters + 1)
+    weights = weights.at[1:].set(cluster_weights - logprob_pi0)
+    weights = weights.at[0].set(-jnp.inf)  # temp, don't stop
+    k = jax.random.categorical(key, weights)
+
+    new_cluster = jax.lax.cond(k==0, 
+        lambda cluster0, cluster1, k, K, max_clusters: trace.cluster, 
+        lambda  cluster0, cluster1, k, K, max_clusters: split_cluster(cluster0, cluster1, k, K, max_clusters),
+        trace.cluster, q_split_trace[-1], k-1, K, max_clusters)
+
+    return new_cluster
+
+def get_weights(trace, K, data, q_split_trace, max_clusters):
+    # for each cluster, get the pi score
+    pi_split = jax.vmap(make_pi, in_axes=(None, 0, None, None))(
+        trace.cluster.pi, jnp.arange(max_clusters), q_split_trace.pi[-1], max_clusters)
+    logpdf_pi = jax.vmap(logpdf, in_axes=(None, 0, None))(trace.gem, pi_split, K)
+
+    logpdf_clusters = score_trace_cluster(data, trace.g, trace.cluster, max_clusters)
+    logpdf_split_clusters = score_trace_cluster(data, trace.g, q_split_trace[-1], max_clusters, add_c=True)
+
+    return logpdf_pi + logpdf_split_clusters - logpdf_clusters
+
+def score_q_pi(q_pi, max_clusters, alpha):
+    q_pi_dist = Dirichlet(alpha=jnp.ones((1, 2)) * alpha/2)
+    q_pi_stack = Categorical(
+        jnp.vstack((
+        jnp.log(q_pi[:max_clusters]),
+        jnp.log(q_pi[max_clusters:]),
+     ))[None, :])
+
+    return jax.vmap(logpdf, in_axes=(None, -1))(q_pi_dist, q_pi_stack)
+
+def make_pi(pi, k, pi_split, max_clusters):
+    pi_k0 = pi[k]
+    pi = pi.at[k].set(pi_k0 * pi_split[k])
+    idx = jnp.argwhere(pi == 0, size=1)
+    pi = pi.at[idx].set(pi_k0 * pi_split[k + max_clusters])
+
+    return jnp.sort(pi, descending=True)
+
+def score_trace_cluster(data, g, cluster, max_clusters, add_c=False):
+    c, pi, f = cluster.c, cluster.pi, cluster.f
+
+    x_scores = jax.vmap(logpdf, in_axes=(None, 0, 0))(f, data, c)
+
+    c = jnp.mod(c, max_clusters)
+
+    pi_dist = Categorical(logprobs=jnp.log(pi.reshape(1, -1)))
+    theta_scores = jax.vmap(logpdf, in_axes=(None, 0))(g, f)[:max_clusters]
+
+    if add_c:
+        c_scores = jax.vmap(logpdf, in_axes=(None, 0))(pi_dist, c.reshape(-1, 1))
+        x_scores = x_scores + c_scores
+
+    xc_scores_cluster = jax.ops.segment_sum(
+        x_scores, c, 
+        num_segments=max_clusters)
+
+    return xc_scores_cluster + theta_scores
+
+def split_cluster(cluster, split_clusters, k, K, max_clusters):
+    # update pi
+    pi = cluster.pi
+    pi0 = pi[k]
+    pi = pi.at[k].set(pi0 * split_clusters.pi[k])    
+    pi = pi.at[K - 1].set(pi0 * split_clusters.pi[k + max_clusters])    
+
+    # update c
+    c = cluster.c
+    c = jnp.where(c == k, split_clusters.c, c)
+    c = jnp.where(c == k + max_clusters, K-1, c)
+
+    # update f
+    f = update_f(cluster.f, split_clusters.f, k, K-1, max_clusters)
+
+    return Cluster(c, pi, f)
+
+@dispatch
+def update_f(f0: Normal, f: Normal, k: Integer[Array, ""], K: Integer[Array, ""], max_clusters: Integer[Array, ""]):
+    mu = update_vector(f0.mu, f.mu, k, K, max_clusters)
+    std = update_vector(f0.std, f.std, k, K, max_clusters)
+
+    return Normal(mu, std)
+
+@dispatch
+def update_f(f0: Categorical, f: Categorical, k: Integer[Array, ""], K: Integer[Array, ""], max_clusters: Integer[Array, ""]):
+    logprobs = update_vector(f0.logprobs, f.logprobs, k, K, max_clusters)
+
+    return Categorical(logprobs)
+
+@dispatch
+def update_f(f0: Mixed, f: Mixed, k: Integer[Array, ""], K: Integer[Array, ""], max_clusters: Integer[Array, ""]):
+    return Mixed(
+        update_f(f0.normal, f.normal, k, K, max_clusters),
+        update_f(f0.categorical, f.categorical, k, K, max_clusters)
+    )
+
+def update_vector(v0, split_v, k, K, max_clusters):
+    v = v0
+    v = v.at[k].set(split_v[k])
+    v = v.at[K].set(split_v[k + max_clusters])
+    return v
+
+@partial(jax.jit, static_argnames=['gibbs_iters', 'max_clusters'])
+def q_split(data, gibbs_iters, max_clusters, key, c0, alpha, g) -> Cluster:
+    keys = jax.random.split(key, 3)
+    c = (c0 + max_clusters * jax.random.bernoulli(keys[0], shape=c0.shape)).astype(int)
+    # c = jnp.concatenate((jnp.zeros(100), jnp.ones(100) * 2))
+
+    log_likelihood_mask = make_log_likelihood_mask(c0, max_clusters)
+
+    partial_gibbs_step = partial(gibbs_step, 
+        alpha=alpha, g=g, data=data, 
+        log_likelihood_mask=log_likelihood_mask, max_clusters=max_clusters)
+
+    keys = jax.random.split(keys[2], gibbs_iters)
+    _, q_split_trace = jax.lax.scan(partial_gibbs_step, c, keys)
+
+
+    return q_split_trace
+
+def make_log_likelihood_mask(c, max_clusters):
+    log_likelihood_mask = -jnp.inf * jnp.ones((c.shape[0], 2 * np.array(max_clusters, dtype=int)))
+
+    n = jnp.arange(c.shape[0], dtype=int)
+    clusters_x = jnp.concatenate((n, n))
+    clusters_y = jnp.concatenate((c, max_clusters + c), dtype=int)
+    return log_likelihood_mask.at[clusters_x, clusters_y].set(0)
+
+def gibbs_step(assignments, key, alpha, g, data, log_likelihood_mask, max_clusters, rejuvenation=False):
+    subkey1, subkey2, subkey3 = jax.random.split(key, 3)
+    f = gibbs_f(max_clusters, data, subkey1, g, assignments)
+    pi = gibbs_pi(max_clusters, subkey2, alpha, assignments, rejuvenation=rejuvenation)
+
+    assignments = gibbs_c(subkey3, pi, log_likelihood_mask, f, data)
+
+    # now compute the logpdf for the assignments given the new distribution
+    return assignments, Cluster(assignments, pi, f)
+
+def gibbs_f(max_clusters, data, key, g, assignments):
+    g_prime = posterior(g, data, assignments, 2*max_clusters)
+    f = sample(key, g_prime)
+    return f
+
+def gibbs_c(key, pi, log_likelihood_mask, f, data):
+    log_likelihoods = jax.vmap(jax.vmap(logpdf, in_axes=(0, None)), in_axes=(None, 0))(f, data)
+    log_likelihoods = log_likelihoods + log_likelihood_mask
+    log_score = log_likelihoods + jnp.log(pi)
+
+    assignments = jax.random.categorical(key, log_score, axis=-1).astype(int)
+    return assignments
+
+def gibbs_pi(max_clusters, key, alpha, c, rejuvenation=False):
+    cluster_counts = jnp.sum(jax.nn.one_hot(c, num_classes=2 * max_clusters, dtype=jnp.int32), axis=0)
+    if rejuvenation:
+        pi = jax.random.dirichlet(key, cluster_counts)
+        return pi
+    else:
+        pi = jax.random.dirichlet(key, alpha / 2 + cluster_counts)
+        pi_pairs = pi.reshape((2, -1))
+        pi_pairs = pi_pairs / jnp.sum(pi_pairs, axis=0)
+        return pi_pairs.reshape(-1)
\ No newline at end of file
diff --git a/src/genjaxmix/utils.py b/src/genjaxmix/utils.py
new file mode 100644
index 0000000..f3b5fca
--- /dev/null
+++ b/src/genjaxmix/utils.py
@@ -0,0 +1,19 @@
+import jax.numpy as jnp
+
+def beta_to_logpi(betas):
+    logb = jnp.log(betas)
+    logb_not = jnp.log(1-betas)
+    C = betas.shape[0]
+    logpi = jnp.zeros(C)
+    for i in range(1,C):
+        logpi = logpi.at[i].set(jnp.sum(logb_not[:i]))
+    for i in range(C):
+        logpi = logpi.at[i].set(logpi[i] + logb[i])
+    return logpi
+
+def logpi_to_beta(logpi):
+    C = logpi.shape[0]
+    betas = logpi[0]*jnp.ones(C)
+    for i in range(1,C):
+        betas = betas.at[i].set(logpi[i]-jnp.sum(jnp.log(-jnp.expm1(betas[:i]))))
+    return betas
\ No newline at end of file
diff --git a/src/genjaxmix/vectorized.py b/src/genjaxmix/vectorized.py
new file mode 100644
index 0000000..b7b7a99
--- /dev/null
+++ b/src/genjaxmix/vectorized.py
@@ -0,0 +1,43 @@
+import jax
+import jax.numpy as jnp
+from genjax import gen, repeat, normal, inverse_gamma, categorical, dirichlet, beta
+from .utils import beta_to_logpi
+
+N = 1000
+K = 5
+F_numerical = 2
+F_categorical = 7
+
+@gen
+def hyperparameters(mu_0=0.0, v_0=1.0, shape=1.0, scale=1.0, alpha=1.0):
+    sigma_sq = inverse_gamma(shape*jnp.ones((K, F_numerical)), scale*jnp.ones((K, F_numerical))) @ "sigma"
+    sigma = jnp.sqrt(sigma_sq)
+    mu = normal(mu_0*jnp.ones((K, F_numerical)), sigma*v_0) @ "mu"
+    # logp = genjax.dirichlet(0.7*jnp.ones((K, L_num))) @ "logp"
+    # logp = jnp.log(logp)
+    return mu, sigma
+
+@gen
+def cluster(pi, mu, sigma):
+    idx = categorical(pi) @ "c"
+    y1 = normal(mu[idx], sigma[idx]) @ "y1"
+    # y2 = genjax.categorical(logp[idx]) @ "y2"
+    return idx, y1
+
+@gen
+def gem(alpha):
+    betas = beta(alpha*jnp.ones(K), jnp.ones(K)) @ "pi"
+    pi = beta_to_logpi(betas)
+    return pi
+
+def generate(N_max):
+    cluster_repeat = repeat(n=N_max)(cluster)
+
+    @gen
+    def dpmm(alpha, mu_0, v_0, a, b):
+        logpi = gem(alpha) @ "pi"
+        mu, sigma = hyperparameters(mu_0, v_0, a, b, 1.0) @ "hyperparameters"
+        y = cluster_repeat(logpi, mu, sigma) @ "assignments"
+        return y
+
+    return dpmm
diff --git a/src/genjaxmix/vectorized_rejuvenation.py b/src/genjaxmix/vectorized_rejuvenation.py
new file mode 100644
index 0000000..5f9f81f
--- /dev/null
+++ b/src/genjaxmix/vectorized_rejuvenation.py
@@ -0,0 +1,119 @@
+from genjax import ChoiceMapBuilder as C
+from genjax import beta, gen, inverse_gamma, normal
+from genjax._src.core.interpreters.incremental import Diff
+from genjax import Pytree
+from genjax._src.generative_functions.distributions.distribution import Distribution
+from genjax.typing import PRNGKey
+from tensorflow_probability.substrates import jax as tfp
+import jax.numpy as jnp
+from .utils import beta_to_logpi
+import jax
+from .vectorized import K
+from .conjugacy import posterior_normal_inverse_gamma
+tfd = tfp.distributions
+
+# def posterior_normal_inverse_gamma(assignments, x):
+    # counts = jnp.bincount(assignments, length=N_max)
+    # # sum_x = jnp.where(counts > 0, jax.ops.segment_sum(x, assignments, N_max)/counts, 0.0)
+    # sum_x = jax.ops.segment_sum(x, assignments, N_max)
+    # sum_x_sq = jax.ops.segment_sum(x**2, assignments, N_max)
+
+    # l_0 = 0.01
+    # m_0 = 1.0
+    # a_0 = 1.0
+    # b_0 = 1.0
+
+    # l = l_0 + counts
+    # m = (l_0 * m_0 + sum_x) / l
+    # a = a_0 + counts / 2
+    # b = b_0 + 0.5 * (sum_x_sq + l_0*m_0**2 - l * m ** 2)
+    # return l,m,a,b
+
+# def posterior_dirichlet(assignments, x):
+#     one_hot_c = jax.nn.one_hot(assignments, N_max)
+#     one_hot_y = jax.nn.one_hot(x, L_num)
+#     frequency_matrix = one_hot_c.T @ one_hot_y
+#     # print(frequency_matrix)
+#     # row_sums = jnp.sum(frequency_matrix, axis=1, keepdims=True)
+#     # row_sums = jnp.where(row_sums == 0, 1, row_sums)
+#     # empirical = frequency_matrix / row_sums
+#     # return jnp.log(empirical)
+#     return frequency_matrix
+
+@gen
+def propose_parameters(obs):
+    _propose_parameters(obs) @ "hyperparameters"
+
+@gen
+def _propose_parameters(obs):
+    c = obs[:, "c"]
+    y1 = obs[:, "y1"]
+
+    mu_0, v_0, a, b = jax.vmap(posterior_normal_inverse_gamma, in_axes=(None, 1))(c, y1)
+    mu_0 = mu_0.T
+    v_0 = v_0.T
+    a = a.T
+    b = b.T
+
+    # Propose sigma
+    sigma_sq = inverse_gamma(a,b) @ "sigma"
+    sigma = jnp.sqrt(sigma_sq)
+
+    # Propose mu
+    mu = normal(mu_0, sigma * v_0 ) @ "mu"
+
+    return mu, sigma
+
+
+def apply_decay(x, gamma):
+    decay_factors = jnp.arange(x.shape[0]) * jnp.log(gamma)
+    logpi = jnp.log(x) + decay_factors 
+    log_max = jnp.max(logpi)
+    log_shifted = logpi - log_max
+    
+    # Compute log-sum-exp for normalization
+    # Normalize in log-space
+    log_norm = jnp.log(jnp.sum(jnp.exp(log_shifted)))
+    logpi = log_shifted - log_norm
+    return logpi
+
+
+@Pytree.dataclass
+class DirichletBeta(Distribution):
+    def random_weighted(self, key: PRNGKey, alpha):
+        sampler = tfd.Dirichlet(concentration = alpha)
+        pi = sampler.sample(seed=key)
+        # logpi = jnp.log(pi)
+        logpi = apply_decay(pi, gamma=0.80)
+
+        def unfold(carry, pi):
+            logbeta = pi - carry
+            return carry + jnp.log(-jnp.expm1(logbeta)) , jnp.exp(logbeta)
+        _, betas = jax.lax.scan(unfold, 0.0, logpi)
+
+        inv_weight = -sampler.log_prob(pi)
+
+        return inv_weight, betas
+
+    def estimate_logpdf(self, key: PRNGKey, betas, alpha):
+        sampler = tfd.Dirichlet(concentration = alpha)
+
+        logpi = beta_to_logpi(betas)
+        pi = jnp.exp(logpi)
+
+        weight = jnp.sum(sampler.log_prob(pi))
+        return weight
+
+dirichlet_beta = DirichletBeta()
+
+@gen 
+def propose_pi(obs):
+    pi = _propose_pi(obs) @ "pi"
+    return pi
+
+@gen
+def _propose_pi(obs):
+    c = obs[:, "c"]
+    proportions = jnp.bincount(c, length = K) + 1e-6
+    pi = dirichlet_beta(proportions) @ "pi"
+    return pi
\ No newline at end of file

From 7a97c447c1043fd1746e42733dacb5c6dcff5404 Mon Sep 17 00:00:00 2001
From: Ian Limarta <ian.limarta@gmail.com>
Date: Wed, 11 Dec 2024 07:33:07 -0600
Subject: [PATCH 2/8] remove old codebase

---
 src/genspn/__init__.py      |   2 -
 src/genspn/distributions.py | 287 ------------------------------------
 src/genspn/io.py            | 177 ----------------------
 src/genspn/smc.py           | 227 ----------------------------
 4 files changed, 693 deletions(-)
 delete mode 100644 src/genspn/__init__.py
 delete mode 100644 src/genspn/distributions.py
 delete mode 100644 src/genspn/io.py
 delete mode 100644 src/genspn/smc.py

diff --git a/src/genspn/__init__.py b/src/genspn/__init__.py
deleted file mode 100644
index 5eb6009..0000000
--- a/src/genspn/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-def hello() -> str:
-    return "Hello from genspn!"
diff --git a/src/genspn/distributions.py b/src/genspn/distributions.py
deleted file mode 100644
index 17b411a..0000000
--- a/src/genspn/distributions.py
+++ /dev/null
@@ -1,287 +0,0 @@
-import jax.numpy as jnp
-import jax
-import equinox as eqx
-from jaxtyping import Array, Float, Integer
-from plum import dispatch
-from typing import Optional
-from numbers import Real
-
-ZERO = 1e-20
-
-class NormalInverseGamma(eqx.Module):
-    m: Float[Array, "*batch n_dim"]
-    l: Float[Array, "*batch n_dim"]
-    a: Float[Array, "*batch n_dim"]
-    b: Float[Array, "*batch n_dim"]
-
-class Dirichlet(eqx.Module):
-    alpha: Float[Array, "*batch n_dim k"]
-
-    def __getitem__(self, key):
-        return Dirichlet(alpha=self.alpha[key])
-
-class Normal(eqx.Module):
-    mu: Float[Array, "*batch n_dim"]
-    std: Float[Array, "*batch n_dim"]
-
-    def __getitem__(self, key):
-        return Normal(mu=self.mu[key], std=self.std[key])
-
-class Categorical(eqx.Module):
-    # assumed normalized, padded
-    logprobs: Float[Array, "*batch n_dim k"]
-    def __getitem__(self, key):
-        return Categorical(logprobs=self.logprobs[key])
-
-type BaseF = Categorical | Normal
-type BaseG = NormalInverseGamma | Dirichlet
-
-class Mixed(eqx.Module):
-    dists: tuple[BaseF, ...]
-
-    def __getitem__(self, key):
-        return Mixed(dists=tuple([dist[key] for dist in self.dists]))
-
-type F = BaseF | Mixed
-
-class MixedConjugate(eqx.Module):
-    dists: tuple[BaseG, ...]
-
-class GEM(eqx.Module):
-    alpha: Float[Array, "*batch"]
-    d: Float[Array, "*batch"]
-
-class Cluster(eqx.Module):
-    c: Float[Array, "*batch n"]
-    pi: Float[Array, "*batch k"]
-    f: Float[Array, "*batch k"]
-
-    def __getitem__(self, key):
-        return Cluster(self.c[key], self.pi[key], self.f[key])
-
-class Trace(eqx.Module):
-    gem: GEM
-    g: NormalInverseGamma | Dirichlet | MixedConjugate
-    cluster: Cluster
-
-
-type BaseDatapoint = Float[Array, "*batch n_c"] | Integer[Array, "*batch n_d"]
-type Datapoint = BaseDatapoint | tuple[BaseDatapoint, ...]
-
-class MixtureModel(eqx.Module):
-    # mask: Datapoint
-    pi: Float[Array, "*batch k"]
-    f: F
-
-@dispatch
-def sample(key: Array, dist: Dirichlet) -> Categorical:
-    probs = jax.random.dirichlet(key, dist.alpha)
-    probs = jnp.where(probs == 0, ZERO, probs)
-
-    return Categorical(jnp.log(probs))
-
-@dispatch 
-def sample(key: Array, dist: NormalInverseGamma) -> Normal:
-    """ See Kevin Murphy's Conjugate Bayesian analysis of the Gaussian distribution:
-    https://www.cs.ubc.ca/~murphyk/Papers/bayesGauss.pdf """
-    keys = jax.random.split(key)
-
-    log_lambda = jax.random.loggamma(key, dist.a) - jnp.log(dist.b)
-    log_sigma = -jnp.log(dist.l) - log_lambda
-    std = jnp.exp(log_sigma/ 2)
-    mu = dist.m + jax.random.normal(keys[1], shape=dist.m.shape) * std
-
-    return Normal(mu=mu, std=jnp.exp(-log_lambda/2))
-
-@dispatch
-def sample(key: Array, dist: MixedConjugate) -> Mixed:
-    keys = jax.random.split(key, len(dist.dists))
-    dists = tuple([sample(keys[i], dist.dists[i]) for i in range(len(dist.dists))])
-
-    return Mixed(dists=dists)
-
-@dispatch
-def sample(key: Array, dist: Normal) -> Float[Array, "n_c"]:
-    return dist.mu + dist.std * jax.random.normal(key, shape=dist.mu.shape)
-
-@dispatch
-def sample(key: Array, dist: Categorical) -> Integer[Array, "n_D"]:
-    return jax.random.categorical(key, dist.logprobs)
-
-@dispatch
-def sample(key: Array, dist: Mixed) -> tuple[Float[Array, "n_c"], Integer[Array, "n_d"]]:
-    keys = jax.random.split(key, len(dist.dists))
-    dists = tuple([sample(keys[i], dist.dists[i]) for i in range(len(dist.dists))])
-    return dists
-
-@dispatch 
-def sample(key: Array, dist: MixtureModel):
-    keys = jax.random.split(key)
-    cluster = jax.random.categorical(keys[0], dist.pi)
-    return sample(key, dist.f[cluster])
-
-@dispatch
-def posterior(dist: MixedConjugate, x: tuple[Float[Array, "batch n_normal_dim"], Integer[Array, "batch n_categorical_dim"]]) -> MixedConjugate:
-    dists = tuple([posterior(dist.dists[i], x[i]) for i in range(len(dist.dists))])
-
-    return MixedConjugate(dists=dists)
-
-@dispatch
-def posterior(dist: MixedConjugate, x: tuple[BaseDatapoint, ...], c: Integer[Array, "batch"], max_clusters:Optional[int]=None) -> MixedConjugate:
-    dists = tuple([posterior(dist.dists[i], x[i], c, max_clusters) for i in range(len(dist.dists))])
-
-    return MixedConjugate(dists=dists)
-
-@dispatch
-def posterior(dist: MixedConjugate, x: Integer[Array, "batch n_dim"], c: Integer[Array, "batch"], max_clusters:Optional[int]=None) -> MixedConjugate:
-    dists = tuple([posterior(dist.dists[i], x[i], c, max_clusters) for i in range(len(dist.dists))])
-
-    return MixedConjugate(dists=dists)
-###
-
-@dispatch
-def posterior(dist: NormalInverseGamma, x: Float[Array, "batch n_dim"], c: Integer[Array, "batch"], max_clusters:Optional[int]=None) -> NormalInverseGamma:
-    N = jax.ops.segment_sum(jnp.ones(x.shape[0], dtype=jnp.int32), c, num_segments=max_clusters)
-    masked_x = jnp.nan_to_num(x, 0.)
-    sum_x = jax.ops.segment_sum(masked_x, c, num_segments=max_clusters)
-    sum_x_sq = jax.ops.segment_sum(masked_x ** 2, c, num_segments=max_clusters)
-
-    return jax.vmap(posterior, in_axes=(None, 0, 0, 0))(dist, N, sum_x, sum_x_sq)
-
-@dispatch
-def posterior(dist: NormalInverseGamma, x: Float[Array, "batch n_dim"]) -> NormalInverseGamma:
-    N = x.shape[0]
-    sum_x = jnp.nansum(x, axis=0)
-    sum_x_sq = jnp.nansum(x ** 2, axis=0)
-
-    return posterior(dist, N, sum_x, sum_x_sq)
-
-@dispatch
-def posterior(dist: NormalInverseGamma, N: Integer[Array, ""], sum_x: Float[Array, "n_dim"], sum_x_sq: Float[Array, "n_dim"]) -> NormalInverseGamma:
-    l = dist.l + N
-    m = (dist.l * dist.m + sum_x) / l
-    a = dist.a + N / 2
-    b = dist.b + 0.5 * (sum_x_sq + dist.l * dist.m ** 2 - l * m ** 2)
-
-    return NormalInverseGamma(m=m, l=l, a=a, b=b)
-
-@dispatch
-def posterior(dist: Dirichlet, x: Integer[Array, "batch n_dim"], c: Integer[Array, "batch"], max_clusters:Optional[int]=None) -> Dirichlet:
-    one_hot_x = jax.nn.one_hot(x, num_classes=dist.alpha.shape[-1], dtype=jnp.int32)
-    counts = jax.ops.segment_sum(one_hot_x, c, num_segments=max_clusters)
-    return jax.vmap(posterior, in_axes=(None, 0))(dist, counts)
-
-@dispatch
-def posterior(dist: Dirichlet, counts: Integer[Array, "n_dim k"]) -> Dirichlet:
-    return Dirichlet(alpha=dist.alpha + counts)
-
-@dispatch
-def logpdf(dist: Normal, x: Float[Array, "n_dim"]) -> Float[Array, ""]:
-    logprob = jnp.nansum(-0.5 * jnp.log(2 * jnp.pi) - jnp.log(dist.std) - 0.5 * ((x - dist.mu) / dist.std) ** 2)
-
-    return logprob
-
-@dispatch
-def logpdf(dist: Categorical, x: Integer[Array, "n_dim"]) -> Float[Array, ""]:
-    return jnp.nansum(dist.logprobs.at[jnp.arange(x.shape[-1]), x].get(mode="fill", fill_value=jnp.nan))
-
-@dispatch
-def logpdf(dist: Mixed, x: Datapoint) -> Float[Array, ""]:
-    return sum([logpdf(dist.dists[i], x[i]) for i in range(len(dist.dists))])
-
-@dispatch
-def logpdf(dist: GEM, pi: Float[Array, "n"], K: Integer[Array, ""]) -> Float[Array, ""]:
-    betas = jax.vmap(lambda i: 1 - pi[i] / pi[i-1])(jnp.arange(len(pi)))
-    betas = betas.at[0].set(pi[0])
-    logprobs = jax.vmap(jax.scipy.stats.beta.logpdf, in_axes=(0, None, 0))(betas, 1-dist.d, dist.alpha + (1 + jnp.arange(len(pi))) * dist.d)
-    idx = jnp.arange(logprobs.shape[0])
-    logprobs = jnp.where(idx < K, logprobs, 0) 
-    return jnp.sum(logprobs)
-
-@dispatch
-def logpdf(dist: F, x: Datapoint, c: Integer[Array, ""]) -> Float[Array, ""]:
-    dist = dist[c]
-    return logpdf(dist, x)
-
-@dispatch
-def logpdf(dist: MixtureModel, x: Datapoint) -> Float[Array, ""]:
-    logprob = jax.vmap(logpdf, in_axes=(0, None))(dist.f, x)
-    logprob = logprob + jnp.log(dist.pi)
-    return jax.scipy.special.logsumexp(logprob)
-
-@dispatch
-def logpdf(dist: MixedConjugate, x: Mixed)-> Float[Array, ""]:
-    return sum([logpdf(dist.dists[i], x.dists[i]) for i in range(len(dist.dists))])
-
-@dispatch
-def logpdf(dist: NormalInverseGamma, x: Normal)-> Float[Array, ""]:
-    """Scores the mu and sigma parameters drawn from an inverse gamma prior.
-
-    Pr[sigma] = Gamma(1/sigma^2; loc=a, scale=1/b)
-    Pr[mu] = Normal(mu |  loc=m, scale=sigma/sqrt(l))
-
-    """
-    std_logpdf = jax.scipy.stats.gamma.logpdf(x.std ** -2, dist.a, scale=1/dist.b)
-    mu_logpdf = jax.scipy.stats.norm.logpdf(x.mu, loc=dist.m, scale=x.std / jnp.sqrt(dist.l))
-    return jnp.sum(mu_logpdf + std_logpdf)
-
-@dispatch
-def logpdf(dist: Dirichlet, x: Categorical)-> Float[Array, ""]:
-    logprobs = jax.vmap(jax.scipy.stats.dirichlet.logpdf)(jnp.exp(x.logprobs), dist.alpha)
-    return jnp.sum(logprobs)
-
-def make_trace(
-        key: jax.Array, alpha: Real, d: Real, 
-        schema: dict,
-        data: Datapoint,
-        max_clusters: int):
-
-    g = make_g(schema)
-
-    n = len(data[0]) if isinstance(data, tuple) else len(data)
-    c = jnp.zeros(n, dtype=int)
-
-    if not isinstance(data, tuple):
-        data = (data,)
-    g_prime = posterior(g, data, c, 2 * max_clusters)
-
-    f = sample(key, g_prime)
-    pi = jnp.zeros(max_clusters)
-    pi = pi.at[0].set(.9)
-    cluster = Cluster(c=c, f=f, pi=pi)
-    gem = GEM(alpha=alpha, d=d)
-
-    return Trace(gem=gem, g=g, cluster=cluster)
-
-def make_g(schema: dict):
-    dists = []
-    if schema["types"]["normal"]:
-        dists.append(make_normal_g(schema))
-    if schema["types"]["categorical"]:
-        dtypes = schema["var_metadata"]["categorical_precisions"]
-        unique_dtypes = list(set(dtypes))
-        for dtype in unique_dtypes:
-            dists.append(make_categorical_g(schema, dtype))
-
-    return MixedConjugate(dists=dists)
-
-def make_normal_g(schema: dict):
-    n_continuous = len(schema["types"]["normal"])
-
-    return NormalInverseGamma(
-        m=jnp.zeros(n_continuous), l=jnp.ones(n_continuous), 
-        a=jnp.ones(n_continuous), b=jnp.ones(n_continuous))
-
-def make_categorical_g(schema: dict, dtype: int):
-    dtypes = schema["var_metadata"]["categorical_precisions"]
-    n_discrete = len([d for d in dtypes if dtype == d])
-    n_categories = jnp.array([len(schema["var_metadata"][col]["levels"]) 
-                              for idx, col in enumerate(schema["types"]["categorical"]) 
-                              if dtypes[idx] == dtype])
-    max_n_categories = jnp.max(n_categories).astype(int)
-
-    cat_alpha = jnp.ones((n_discrete, max_n_categories))
-    mask = jnp.tile(jnp.arange(max_n_categories), (n_discrete, 1)) < n_categories[:, None]
-    cat_alpha = jnp.where(mask, cat_alpha, ZERO)
-
-    return Dirichlet(alpha=cat_alpha)
diff --git a/src/genspn/io.py b/src/genspn/io.py
deleted file mode 100644
index 2a22597..0000000
--- a/src/genspn/io.py
+++ /dev/null
@@ -1,177 +0,0 @@
-import polars as pl
-import polars.selectors as cs
-import numpy as np
-import jax
-import jax.numpy as jnp
-from jaxtyping import Array, Float, Integer, Num
-from plum import dispatch
-import os
-from safetensors import safe_open
-from safetensors.flax import save_file
-
-
-def dataframe_to_arrays(df: pl.DataFrame):
-    schema = make_schema(df)
-    categorical_df = df.select(schema["types"]["categorical"])
-    numerical_df = df.select(schema["types"]["normal"])
-
-    def normalize(col: pl.Expr):
-        return (col - schema["var_metadata"][col.name]["mean"]) / schema["var_metadata"][col.name]["std"]
-
-    numerical_df = numerical_df.with_columns(
-        pl.all().map_batches(normalize)
-    )
-
-    numerical_array  = None if numerical_df.is_empty() else jnp.array(numerical_df.to_numpy())
-    categorical_arrays, schema = (None, schema) if categorical_df.is_empty() else categorical_df_to_integer(categorical_df, schema)
-
-    return schema, (numerical_array, *categorical_arrays)
-
-
-def categorical_df_to_integer(df: pl.DataFrame, schema: dict):
-    def cast_to_categorical(col: pl.Expr):
-        return col.cast(pl.Enum(schema["var_metadata"][col.name]["levels"]))
-
-    df = df.with_columns(pl.all().map_batches(cast_to_categorical))
-
-    array = df.with_columns(pl.all().to_physical()).to_numpy()
-    array = jnp.array(array)
-
-    all_n_categories = np.nanmax(array, axis=0)
-    dtypes = [get_dtype(n_categories) for n_categories in all_n_categories]
-    unique_dtypes = list(set(dtypes))
-
-    arrays = []
-    for dtype in unique_dtypes:
-        idxs = np.where(np.array(dtypes) == dtype)[0]
-        arrays.append(jnp.nan_to_num(array[:, idxs], nan=jnp.iinfo(dtype).max).astype(dtype))
-
-    dtype_idxs = [unique_dtypes.index(dtype) for dtype in dtypes]
-    schema["var_metadata"]["categorical_precisions"] = dtype_idxs
-
-    return arrays, schema
-
-def get_dtype(n_categories):
-    match n_categories:
-        case n_categories if n_categories < jnp.iinfo(jnp.uint4).max:
-            dtype = jnp.uint8  # uint4 currently not supported by jax
-        case n_categories if n_categories < jnp.iinfo(jnp.uint8).max:
-            dtype = jnp.uint8
-        case n_categories if n_categories < jnp.iinfo(jnp.uint16).max:
-            dtype = jnp.uint16
-        case n_categories if n_categories < jnp.iinfo(jnp.uint32).max:
-            dtype = jnp.uint32
-        case n_categories if n_categories < jnp.iinfo(jnp.uint64).max:
-            dtype = jnp.uint64
-        case _:
-            raise ValueError(n_categories)
-
-    return dtype
-
-def load_huggingface(dataset_path):
-    splits = {
-        "train": f"{dataset_path}/data-train.parquet",
-        "test": f"{dataset_path}/data-test.parquet"
-    }
-    train_df = pl.read_parquet(f"hf://datasets/Large-Population-Model/model-building-evaluation/{splits['train']}")
-    test_df = pl.read_parquet(f"hf://datasets/Large-Population-Model/model-building-evaluation/{splits['test']}")
-
-    df = pl.concat((train_df, test_df))
-    schema, (numerical_array, categorical_array) = dataframe_to_arrays(df)
-
-    n_train = len(train_df)
-
-    if numerical_array is None:
-        return schema, (categorical_array[:n_train], categorical_array[n_train:])
-    elif categorical_array is None:
-        return schema, (numerical_array[:n_train], numerical_array[n_train:])
-    else:
-        return schema, ((numerical_array[:n_train], categorical_array[:n_train]), (numerical_array[n_train:], categorical_array[n_train:]))
-
-
-def _get_indices(n, seed: int):
-    """"Create a random permutation of indices using the provided seed."""
-    rng = np.random.default_rng(seed)
-    return rng.permutation(n)
-
-
-@dispatch
-def split_data(data: tuple[Float[Array, "n n_c"], Integer[Array, "n n_d"]], test_ratio: float = 0.2, seed: int = 42):
-    # Unpack the train_data tuple
-    data_numerical, data_categorical = data
-
-    # Calculate the number of samples for the train set
-    n_samples = data_numerical.shape[0]
-    n_train = int((1 - test_ratio) * n_samples)
-
-    # Create a random permutation of indices
-    indices = _get_indices(n_samples, seed)
-
-    # Split the numerical data
-    train_numerical, test_numerical = data_numerical[indices[:n_train]], data_numerical[indices[n_train:]]
-
-    # Split the categorical data
-    train_categorical, test_categorical = data_categorical[indices[:n_train]], data_categorical[indices[n_train:]]
-
-    # Recombine the split data into tuples
-    train_data = (train_numerical, train_categorical)
-    test_data = (test_numerical, test_categorical)
-
-    return train_data, test_data
-
-
-@dispatch
-def split_data(data: Float[Array, "n n_c"] | Integer[Array, "n n_d"], test_ratio: float = 0.2, seed: int = 42):
-    # Calculate the number of samples for the train set (80% of the data)
-    n_samples = data.shape[0]
-    n_train = int((1 - test_ratio) * n_samples)
-
-    # Create a random permutation of indices
-    indices = _get_indices(n_samples, seed)
-
-    # Split the numerical data
-    train_data, test_data = data[indices[:n_train]], data[indices[n_train:]]
-
-    return train_data, test_data
-
-def make_schema(df: pl.DataFrame):
-    schema = {
-        "types":{
-            "normal": [],
-            "categorical": []
-        },
-        "var_metadata":{}
-    }
-    for c in df.columns:
-        if df[c].dtype == pl.Utf8:
-            schema["types"]["categorical"].append(c)
-            schema["var_metadata"][c] = {"levels": df[c].drop_nulls().unique().sort().to_list()}
-        elif df[c].dtype == pl.Float64:
-            schema["types"]["normal"].append(c)
-            schema["var_metadata"][c] = {"mean": df[c].mean(), "std": df[c].std()}
-        else:
-            raise ValueError(c)
-    return schema
-
-
-def _assert_keys_mixture(mixture_parameters):
-    heterogeneous = {"cluster_weights", "mu", "sigma", "logprobs"}
-    numerical = {"cluster_weights", "mu", "sigma"}
-    categorical = {"cluster_weights", "logprobs"}
-    assert set(mixture_parameters.keys()) == heterogeneous or \
-            set(mixture_parameters.keys()) == numerical or \
-            set(mixture_parameters.keys()) == categorical, \
-            "wrong keys for parameter record. pi cannot be null;" + \
-            "either mu and std are not null or logprobs are not null"
-
-def serialize(mixture_parameters, path):
-    _assert_keys_mixture(mixture_parameters)
-    save_file(mixture_parameters, path)
-
-def deserialize(path):
-    mixture_parameters = {}
-    with safe_open(path, framework="flax", device="cpu") as f:
-        for key in f.keys():
-           mixture_parameters[key] = f.get_tensor(key)
-    _assert_keys_mixture(mixture_parameters)
-    return mixture_parameters
diff --git a/src/genspn/smc.py b/src/genspn/smc.py
deleted file mode 100644
index f29843c..0000000
--- a/src/genspn/smc.py
+++ /dev/null
@@ -1,227 +0,0 @@
-import jax.numpy as jnp
-import jax
-import numpy as np
-from plum import dispatch
-from jaxtyping import Array, Integer
-from genspn.distributions import (Dirichlet, MixtureModel,
-    posterior, sample, logpdf, Normal, Categorical, Mixed, Cluster, Trace)
-from functools import partial
-
-@partial(jax.jit, static_argnames=['gibbs_iters', 'max_clusters', 'n_steps'])
-def smc(key, trace, data_test, n_steps, data, gibbs_iters, max_clusters):
-
-    if not isinstance(data, tuple):
-        data = (data,)
-        data_test = (data_test,)
-    smc_keys = jax.random.split(key, n_steps)
-
-    def wrap_step(trace, n):
-        key = smc_keys[n]
-        keys = jax.random.split(key, 3)
-        new_cluster = step(data=data, trace=trace, gibbs_iters=gibbs_iters, 
-            max_clusters=max_clusters, key=keys[0], K=n+2)
-        split_trace = Trace(
-            gem=trace.gem,
-            g=trace.g,
-            cluster=new_cluster
-        )
-
-        rejuvenated_cluster = rejuvenate(keys[1], data, split_trace, gibbs_iters, max_clusters)
-        rejuvenated_trace = Trace(
-            gem=split_trace.gem,
-            g=trace.g,
-            cluster=rejuvenated_cluster
-        )
-
-        mixture_model = MixtureModel(
-            pi=rejuvenated_trace.cluster.pi/jnp.sum(rejuvenated_trace.cluster.pi), 
-            f=rejuvenated_trace.cluster.f[:max_clusters])
-        logprobs = jax.vmap(logpdf, in_axes=(None, 0))(mixture_model, data_test)
-        sum_logprobs = jnp.sum(logprobs)
-
-        return rejuvenated_trace, (rejuvenated_trace, sum_logprobs)
-
-    carry, (trace, sum_logprobs) = jax.lax.scan(wrap_step, trace, jnp.arange(n_steps))
-    return trace, sum_logprobs
-
-
-
-def rejuvenate(key, data, trace, gibbs_iters, max_clusters):
-    extended_pi = jnp.concatenate((trace.cluster.pi, jnp.zeros(max_clusters)))
-    log_likelihood_mask = jnp.where(extended_pi == 0, -jnp.inf, 0)
-
-    partial_gibbs_step = partial(gibbs_step, 
-        alpha=trace.gem.alpha, g=trace.g, data=data, 
-        log_likelihood_mask=log_likelihood_mask, max_clusters=max_clusters,
-        rejuvenation=True)
-
-    keys = jax.random.split(key, gibbs_iters)
-    _, q_split_trace = jax.lax.scan(partial_gibbs_step, trace.cluster.c, keys)
-
-    cluster = q_split_trace[-1]
-    cluster = Cluster(cluster.c, jnp.sum(trace.cluster.pi) * cluster.pi[:max_clusters], cluster.f)
-
-    return cluster
-
-@partial(jax.jit, static_argnames=['gibbs_iters', 'max_clusters'])
-def step(data, gibbs_iters, key, K, trace, max_clusters):
-    q_split_trace = q_split(data, gibbs_iters, max_clusters, key, trace.cluster.c, trace.gem.alpha, trace.g)
-
-    cluster_weights = get_weights(trace, K, data, q_split_trace, max_clusters)
-
-    logprob_pi0 = logpdf(trace.gem, jnp.sort(trace.cluster.pi, descending=True), K-1)
-
-    weights = jnp.zeros(max_clusters + 1)
-    weights = weights.at[1:].set(cluster_weights - logprob_pi0)
-    weights = weights.at[0].set(-jnp.inf)  # temp, don't stop
-    k = jax.random.categorical(key, weights)
-
-    new_cluster = jax.lax.cond(k==0, 
-        lambda cluster0, cluster1, k, K, max_clusters: trace.cluster, 
-        lambda  cluster0, cluster1, k, K, max_clusters: split_cluster(cluster0, cluster1, k, K, max_clusters),
-        trace.cluster, q_split_trace[-1], k-1, K, max_clusters)
-
-    return new_cluster
-
-def get_weights(trace, K, data, q_split_trace, max_clusters):
-    # for each cluster, get the pi score
-    pi_split = jax.vmap(make_pi, in_axes=(None, 0, None, None))(
-        trace.cluster.pi, jnp.arange(max_clusters), q_split_trace.pi[-1], max_clusters)
-    logpdf_pi = jax.vmap(logpdf, in_axes=(None, 0, None))(trace.gem, pi_split, K)
-
-    logpdf_clusters = score_trace_cluster(data, trace.g, trace.cluster, max_clusters)
-    logpdf_split_clusters = score_trace_cluster(data, trace.g, q_split_trace[-1], max_clusters, add_c=True)
-
-    return logpdf_pi + logpdf_split_clusters - logpdf_clusters
-
-def make_pi(pi, k, pi_split, max_clusters):
-    pi_k0 = pi[k]
-    pi = pi.at[k].set(pi_k0 * pi_split[k])
-    idx = jnp.argwhere(pi == 0, size=1)
-    pi = pi.at[idx].set(pi_k0 * pi_split[k + max_clusters])
-
-    return jnp.sort(pi, descending=True)
-
-def score_trace_cluster(data, g, cluster, max_clusters, add_c=False):
-    c, pi, f = cluster.c, cluster.pi, cluster.f
-
-    x_scores = jax.vmap(logpdf, in_axes=(None, 0, 0))(f, data, c)
-
-    c = jnp.mod(c, max_clusters)
-
-    pi_dist = Categorical(logprobs=jnp.log(pi.reshape(1, -1)))
-    theta_scores = jax.vmap(logpdf, in_axes=(None, 0))(g, f)[:max_clusters]
-
-    if add_c:
-        c_scores = jax.vmap(logpdf, in_axes=(None, 0))(pi_dist, c.reshape(-1, 1))
-        x_scores = x_scores + c_scores
-
-    xc_scores_cluster = jax.ops.segment_sum(
-        x_scores, c, 
-        num_segments=max_clusters)
-
-    return xc_scores_cluster + theta_scores
-
-def split_cluster(cluster, split_clusters, k, K, max_clusters):
-    # update pi
-    pi = cluster.pi
-    pi0 = pi[k]
-    pi = pi.at[k].set(pi0 * split_clusters.pi[k])    
-    pi = pi.at[K - 1].set(pi0 * split_clusters.pi[k + max_clusters])    
-
-    # update c
-    c = cluster.c
-    c = jnp.where(c == k, split_clusters.c, c)
-    c = jnp.where(c == k + max_clusters, K-1, c)
-
-    # update f
-    f = update_f(cluster.f, split_clusters.f, k, K-1, max_clusters)
-
-    return Cluster(c, pi, f)
-
-@dispatch
-def update_f(f0: Normal, f: Normal, k: Integer[Array, ""], K: Integer[Array, ""], max_clusters: Integer[Array, ""]):
-    mu = update_vector(f0.mu, f.mu, k, K, max_clusters)
-    std = update_vector(f0.std, f.std, k, K, max_clusters)
-
-    return Normal(mu, std)
-
-@dispatch
-def update_f(f0: Categorical, f: Categorical, k: Integer[Array, ""], K: Integer[Array, ""], max_clusters: Integer[Array, ""]):
-    logprobs = update_vector(f0.logprobs, f.logprobs, k, K, max_clusters)
-
-    return Categorical(logprobs)
-
-@dispatch
-# plum is struggling with this signature for some reason, momentarily using a catch all
-# def update_f(f0: Mixed, f: Mixed, k: Integer[Array, ""], K: Integer[Array, ""], max_clusters: Integer[Array, ""]):
-def update_f(f0: Mixed, f, k, K, max_clusters):
-    return Mixed(
-        dists=tuple([update_f(f0.dists[i], f.dists[i], k, K, max_clusters) for i in range(len(f0.dists))])
-    )
-
-def update_vector(v0, split_v, k, K, max_clusters):
-    v = v0
-    v = v.at[k].set(split_v[k])
-    v = v.at[K].set(split_v[k + max_clusters])
-    return v
-
-@partial(jax.jit, static_argnames=['gibbs_iters', 'max_clusters'])
-def q_split(data, gibbs_iters, max_clusters, key, c0, alpha, g) -> Cluster:
-    keys = jax.random.split(key, 3)
-    c = (c0 + max_clusters * jax.random.bernoulli(keys[0], shape=c0.shape)).astype(int)
-    # c = jnp.concatenate((jnp.zeros(100), jnp.ones(100) * 2))
-
-    log_likelihood_mask = make_log_likelihood_mask(c0, max_clusters)
-
-    partial_gibbs_step = partial(gibbs_step, 
-        alpha=alpha, g=g, data=data, 
-        log_likelihood_mask=log_likelihood_mask, max_clusters=max_clusters)
-
-    keys = jax.random.split(keys[2], gibbs_iters)
-    _, q_split_trace = jax.lax.scan(partial_gibbs_step, c, keys)
-
-
-    return q_split_trace
-
-def make_log_likelihood_mask(c, max_clusters):
-    log_likelihood_mask = -jnp.inf * jnp.ones((c.shape[0], 2 * np.array(max_clusters, dtype=int)))
-
-    n = jnp.arange(c.shape[0], dtype=int)
-    clusters_x = jnp.concatenate((n, n))
-    clusters_y = jnp.concatenate((c, max_clusters + c), dtype=int)
-    return log_likelihood_mask.at[clusters_x, clusters_y].set(0)
-
-def gibbs_step(assignments, key, alpha, g, data, log_likelihood_mask, max_clusters, rejuvenation=False):
-    subkey1, subkey2, subkey3 = jax.random.split(key, 3)
-    f = gibbs_f(max_clusters, data, subkey1, g, assignments)
-    pi = gibbs_pi(max_clusters, subkey2, alpha, assignments, rejuvenation=rejuvenation)
-
-    assignments = gibbs_c(subkey3, pi, log_likelihood_mask, f, data)
-
-    # now compute the logpdf for the assignments given the new distribution
-    return assignments, Cluster(assignments, pi, f)
-
-def gibbs_f(max_clusters, data, key, g, assignments):
-    g_prime = posterior(g, data, assignments, 2*max_clusters)
-    f = sample(key, g_prime)
-    return f
-
-def gibbs_c(key, pi, log_likelihood_mask, f, data):
-    log_likelihoods = jax.vmap(jax.vmap(logpdf, in_axes=(0, None)), in_axes=(None, 0))(f, data)
-    log_likelihoods = log_likelihoods + log_likelihood_mask
-    log_score = log_likelihoods + jnp.log(pi)
-
-    assignments = jax.random.categorical(key, log_score, axis=-1).astype(int)
-    return assignments
-
-def gibbs_pi(max_clusters, key, alpha, c, rejuvenation=False):
-    cluster_counts = jnp.sum(jax.nn.one_hot(c, num_classes=2 * max_clusters, dtype=jnp.int32), axis=0)
-    if rejuvenation:
-        pi = jax.random.dirichlet(key, cluster_counts)
-        return pi
-    else:
-        pi = jax.random.dirichlet(key, alpha / 2 + cluster_counts)
-        pi_pairs = pi.reshape((2, -1))
-        pi_pairs = pi_pairs / jnp.sum(pi_pairs, axis=0)
-        return pi_pairs.reshape(-1)

From fa3bf8533ef5e8f371b2d07dc2bac11bb828774d Mon Sep 17 00:00:00 2001
From: Ian Limarta <ian.limarta@gmail.com>
Date: Fri, 13 Dec 2024 13:14:19 -0500
Subject: [PATCH 3/8] switch to mkdocs

---
 docs/api.md                       |   0
 docs/dev.md                       |   0
 docs/index.md                     |  16 +
 docs/tutorials/getting_started.md |  18 +
 docs/tutorials/heart_disease.md   |   1 +
 docs/tutorials/test.py            |   1 +
 mkdocs.yml                        |  23 ++
 pyproject.toml                    |  10 +
 uv.lock                           | 525 ++++++++++++++++++++++++++++++
 9 files changed, 594 insertions(+)
 create mode 100644 docs/api.md
 create mode 100644 docs/dev.md
 create mode 100644 docs/index.md
 create mode 100644 docs/tutorials/getting_started.md
 create mode 100644 docs/tutorials/heart_disease.md
 create mode 100644 docs/tutorials/test.py
 create mode 100644 mkdocs.yml

diff --git a/docs/api.md b/docs/api.md
new file mode 100644
index 0000000..e69de29
diff --git a/docs/dev.md b/docs/dev.md
new file mode 100644
index 0000000..e69de29
diff --git a/docs/index.md b/docs/index.md
new file mode 100644
index 0000000..45f4821
--- /dev/null
+++ b/docs/index.md
@@ -0,0 +1,16 @@
+## What is GenJAXMix?
+GenJAXMix is Dirichlet Process Mixture Modeling framework written in the JAX-accelerated probabilistic programming language `GenJAX`. Thus it provides a fast implementation to model and infer clusters using DPMMs on the GPU using clean *Genial* programs.
+
+## Quickstart
+Currently GenJAXMix is currently *private*, so please install it directly from Github:
+
+```
+pip install git+git@github.com:OpenGen/genjaxmix.git
+```
+
+## For Developers
+GenJAXMix uses `uv` for package management and development. To set up GenJAXMix for development, please install `uv` and run
+```
+uv sync
+```
+
diff --git a/docs/tutorials/getting_started.md b/docs/tutorials/getting_started.md
new file mode 100644
index 0000000..41ce592
--- /dev/null
+++ b/docs/tutorials/getting_started.md
@@ -0,0 +1,18 @@
+<!-- [TOC] -->
+
+This tutorial introduces Dirichlet Process Mixture Models and explores how to cluster using a simple example.
+
+# Dataset
+First we need a dataset. In this tutorial, we will synthetically generate points on on the 2D plane and form small clusters that we expect to later detect during inference. We will generate data using a [Gaussian Mixture Model](https://en.wikipedia.org/wiki/Mixture_model#Gaussian_mixture_model) in pure JAX and later feed in this dataset to GenJAXMix.
+
+
+
+# Generative Model
+
+# Inference 
+
+## Inferring Cluster Means
+
+## Inferring Cluster Weights
+
+## Inferring Data Point Membership
diff --git a/docs/tutorials/heart_disease.md b/docs/tutorials/heart_disease.md
new file mode 100644
index 0000000..ab405f5
--- /dev/null
+++ b/docs/tutorials/heart_disease.md
@@ -0,0 +1 @@
+Something here...
\ No newline at end of file
diff --git a/docs/tutorials/test.py b/docs/tutorials/test.py
new file mode 100644
index 0000000..ed7dfc3
--- /dev/null
+++ b/docs/tutorials/test.py
@@ -0,0 +1 @@
+import jax
\ No newline at end of file
diff --git a/mkdocs.yml b/mkdocs.yml
new file mode 100644
index 0000000..c4bc3c3
--- /dev/null
+++ b/mkdocs.yml
@@ -0,0 +1,23 @@
+site_name: GenJAXMix
+
+theme:
+  name: "material"
+  # features:
+  #   - navigation.tabs
+  #   - toc.follow
+  #   - toc.integrate
+
+# markdown_extensions:
+#   - toc
+
+plugins:
+  - mkdocs-jupyter
+
+nav: 
+  - Home: index.md
+  - Tutorials: 
+    - Getting Started: tutorials/getting_started.md
+    - Analyzing Heart Diesease Patients: tutorials/heart_disease.md
+    - Test: tutorials/test.py
+  - Library Reference: api.md
+  - For Developers: dev.md
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
index 5284dfb..0686661 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -26,3 +26,13 @@ genjax = { git = "https://github.com/probcomp/genjax.git" }
 [[tool.uv.index]]
 name="gcp"
 url="https://us-west1-python.pkg.dev/probcomp-caliban/probcomp/simple/"
+
+[dependency-groups]
+dev = [
+    "mkdocs-material>=9.5.48",
+    "mkdocs>=1.6.1",
+    "mkdocstrings>=0.27.0",
+    "mkdocstrings-python>=1.12.2",
+    "mkdocs-jupyter>=0.25.1",
+    "jupytext>=1.16.4",
+]
diff --git a/uv.lock b/uv.lock
index 0b009c6..62ae0bd 100644
--- a/uv.lock
+++ b/uv.lock
@@ -60,6 +60,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/6a/21/5b6702a7f963e95456c0de2d495f67bf5fd62840ac655dc451586d23d39a/attrs-24.2.0-py3-none-any.whl", hash = "sha256:81921eb96de3191c8258c199618104dd27ac608d9366f5e35d011eae1867ede2", size = 63001 },
 ]
 
+[[package]]
+name = "babel"
+version = "2.16.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/2a/74/f1bc80f23eeba13393b7222b11d95ca3af2c1e28edca18af487137eefed9/babel-2.16.0.tar.gz", hash = "sha256:d1f3554ca26605fe173f3de0c65f750f5a42f924499bf134de6423582298e316", size = 9348104 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ed/20/bc79bc575ba2e2a7f70e8a1155618bb1301eaa5132a8271373a6903f73f8/babel-2.16.0-py3-none-any.whl", hash = "sha256:368b5b98b37c06b7daf6696391c3240c938b37767d4584413e8438c5c435fa8b", size = 9587599 },
+]
+
 [[package]]
 name = "beartype"
 version = "0.18.5"
@@ -69,6 +78,30 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/64/43/7a1259741bd989723272ac7d381a43be932422abcff09a1d9f7ba212cb74/beartype-0.18.5-py3-none-any.whl", hash = "sha256:5301a14f2a9a5540fe47ec6d34d758e9cd8331d36c4760fc7a5499ab86310089", size = 917762 },
 ]
 
+[[package]]
+name = "beautifulsoup4"
+version = "4.12.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "soupsieve" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/b3/ca/824b1195773ce6166d388573fc106ce56d4a805bd7427b624e063596ec58/beautifulsoup4-4.12.3.tar.gz", hash = "sha256:74e3d1928edc070d21748185c46e3fb33490f22f52a3addee9aee0f4f7781051", size = 581181 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/b1/fe/e8c672695b37eecc5cbf43e1d0638d88d66ba3a44c4d321c796f4e59167f/beautifulsoup4-4.12.3-py3-none-any.whl", hash = "sha256:b80878c9f40111313e55da8ba20bdba06d8fa3969fc68304167741bbf9e082ed", size = 147925 },
+]
+
+[[package]]
+name = "bleach"
+version = "6.2.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "webencodings" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/76/9a/0e33f5054c54d349ea62c277191c020c2d6ef1d65ab2cb1993f91ec846d1/bleach-6.2.0.tar.gz", hash = "sha256:123e894118b8a599fd80d3ec1a6d4cc7ce4e5882b1317a7e1ba69b56e95f991f", size = 203083 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/fc/55/96142937f66150805c25c4d0f31ee4132fd33497753400734f9dfdcbdc66/bleach-6.2.0-py3-none-any.whl", hash = "sha256:117d9c6097a7c3d22fd578fcd8d35ff1e125df6736f554da4e432fdd63f31e5e", size = 163406 },
+]
+
 [[package]]
 name = "cachetools"
 version = "5.5.0"
@@ -159,6 +192,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/bf/9b/08c0432272d77b04803958a4598a51e2a4b51c06640af8b8f0f908c18bf2/charset_normalizer-3.4.0-py3-none-any.whl", hash = "sha256:fe9f97feb71aa9896b81973a7bbada8c49501dc73e58a10fcef6663af95e5079", size = 49446 },
 ]
 
+[[package]]
+name = "click"
+version = "8.1.7"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "colorama", marker = "platform_system == 'Windows'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/96/d3/f04c7bfcf5c1862a2a5b845c6b2b360488cf47af55dfa79c98f6a6bf98b5/click-8.1.7.tar.gz", hash = "sha256:ca9853ad459e787e2192211578cc907e7594e294c7ccc834310722b41b9ca6de", size = 336121 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/00/2e/d53fa4befbf2cfa713304affc7ca780ce4fc1fd8710527771b58311a3229/click-8.1.7-py3-none-any.whl", hash = "sha256:ae74fb96c20a0277a1d615f1e4d73c8414f5a98db8b799a7931d1582f3390c28", size = 97941 },
+]
+
 [[package]]
 name = "cloudpickle"
 version = "3.1.0"
@@ -288,6 +333,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/d5/50/83c593b07763e1161326b3b8c6686f0f4b0f24d5526546bee538c89837d6/decorator-5.1.1-py3-none-any.whl", hash = "sha256:b8c3f85900b9dc423225913c5aace94729fe1fa9763b38939a95226f02d37186", size = 9073 },
 ]
 
+[[package]]
+name = "defusedxml"
+version = "0.7.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/0f/d5/c66da9b79e5bdb124974bfe172b4daf3c984ebd9c2a06e2b8a4dc7331c72/defusedxml-0.7.1.tar.gz", hash = "sha256:1bb3032db185915b62d7c6209c5a8792be6a32ab2fedacc84e01b52c51aa3e69", size = 75520 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/07/6c/aa3f2f849e01cb6a001cd8554a88d4c77c5c1a31c95bdf1cf9301e6d9ef4/defusedxml-0.7.1-py2.py3-none-any.whl", hash = "sha256:a352e7e428770286cc899e2542b6cdaedb2b4953ff269a210103ec58f6198a61", size = 25604 },
+]
+
 [[package]]
 name = "deprecated"
 version = "1.2.15"
@@ -324,6 +378,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/b5/fd/afcd0496feca3276f509df3dbd5dae726fcc756f1a08d9e25abe1733f962/executing-2.1.0-py2.py3-none-any.whl", hash = "sha256:8d63781349375b5ebccc3142f4b30350c0cd9c79f921cde38be2be4637e98eaf", size = 25805 },
 ]
 
+[[package]]
+name = "fastjsonschema"
+version = "2.21.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/8b/50/4b769ce1ac4071a1ef6d86b1a3fb56cdc3a37615e8c5519e1af96cdac366/fastjsonschema-2.21.1.tar.gz", hash = "sha256:794d4f0a58f848961ba16af7b9c85a3e88cd360df008c59aac6fc5ae9323b5d4", size = 373939 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/90/2b/0817a2b257fe88725c25589d89aec060581aabf668707a8d03b2e9e0cb2a/fastjsonschema-2.21.1-py3-none-any.whl", hash = "sha256:c9e5b7e908310918cf494a434eeb31384dd84a98b57a30bcb1f535015b554667", size = 23924 },
+]
+
 [[package]]
 name = "filelock"
 version = "3.16.1"
@@ -411,6 +474,16 @@ dependencies = [
     { name = "pytest" },
 ]
 
+[package.dev-dependencies]
+dev = [
+    { name = "jupytext" },
+    { name = "mkdocs" },
+    { name = "mkdocs-jupyter" },
+    { name = "mkdocs-material" },
+    { name = "mkdocstrings" },
+    { name = "mkdocstrings-python" },
+]
+
 [package.metadata]
 requires-dist = [
     { name = "altair", specifier = ">=5.5.0" },
@@ -428,6 +501,28 @@ requires-dist = [
     { name = "pytest", specifier = ">=8.3.3" },
 ]
 
+[package.metadata.requires-dev]
+dev = [
+    { name = "jupytext", specifier = ">=1.16.4" },
+    { name = "mkdocs", specifier = ">=1.6.1" },
+    { name = "mkdocs-jupyter", specifier = ">=0.25.1" },
+    { name = "mkdocs-material", specifier = ">=9.5.48" },
+    { name = "mkdocstrings", specifier = ">=0.27.0" },
+    { name = "mkdocstrings-python", specifier = ">=1.12.2" },
+]
+
+[[package]]
+name = "ghp-import"
+version = "2.1.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "python-dateutil" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/d9/29/d40217cbe2f6b1359e00c6c307bb3fc876ba74068cbab3dde77f03ca0dc4/ghp-import-2.1.0.tar.gz", hash = "sha256:9c535c4c61193c2df8871222567d7fd7e5014d835f97dc7b7439069e2413d343", size = 10943 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f7/ec/67fbef5d497f86283db54c22eec6f6140243aae73265799baaaa19cd17fb/ghp_import-2.1.0-py3-none-any.whl", hash = "sha256:8337dd7b50877f163d4c0289bc1f1c7f127550241988d568c1db512c4324a619", size = 11034 },
+]
+
 [[package]]
 name = "google-auth"
 version = "2.36.0"
@@ -442,6 +537,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/2d/9a/3d5087d27865c2f0431b942b5c4500b7d1b744dd3262fdc973a4c39d099e/google_auth-2.36.0-py2.py3-none-any.whl", hash = "sha256:51a15d47028b66fd36e5c64a82d2d57480075bccc7da37cde257fc94177a61fb", size = 209519 },
 ]
 
+[[package]]
+name = "griffe"
+version = "1.5.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "colorama" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/d4/c9/8167810358ca129839156dc002526e7398b5fad4a9d7b6e88b875e802d0d/griffe-1.5.1.tar.gz", hash = "sha256:72964f93e08c553257706d6cd2c42d1c172213feb48b2be386f243380b405d4b", size = 384113 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ab/00/e693a155da0a2a72fd2df75b8fe338146cae59d590ad6f56800adde90cb5/griffe-1.5.1-py3-none-any.whl", hash = "sha256:ad6a7980f8c424c9102160aafa3bcdf799df0e75f7829d75af9ee5aef656f860", size = 127132 },
+]
+
 [[package]]
 name = "huggingface-hub"
 version = "0.26.2"
@@ -722,6 +829,31 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/c9/fb/108ecd1fe961941959ad0ee4e12ee7b8b1477247f30b1fdfd83ceaf017f0/jupyter_core-5.7.2-py3-none-any.whl", hash = "sha256:4f7315d2f6b4bcf2e3e7cb6e46772eba760ae459cd1f59d29eb57b0a01bd7409", size = 28965 },
 ]
 
+[[package]]
+name = "jupyterlab-pygments"
+version = "0.3.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/90/51/9187be60d989df97f5f0aba133fa54e7300f17616e065d1ada7d7646b6d6/jupyterlab_pygments-0.3.0.tar.gz", hash = "sha256:721aca4d9029252b11cfa9d185e5b5af4d54772bb8072f9b7036f4170054d35d", size = 512900 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/b1/dd/ead9d8ea85bf202d90cc513b533f9c363121c7792674f78e0d8a854b63b4/jupyterlab_pygments-0.3.0-py3-none-any.whl", hash = "sha256:841a89020971da1d8693f1a99997aefc5dc424bb1b251fd6322462a1b8842780", size = 15884 },
+]
+
+[[package]]
+name = "jupytext"
+version = "1.16.4"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "markdown-it-py" },
+    { name = "mdit-py-plugins" },
+    { name = "nbformat" },
+    { name = "packaging" },
+    { name = "pyyaml" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/a1/ba/81097573072b165772b71298c339d5da46dfeec53c1c354ce282109967ea/jupytext-1.16.4.tar.gz", hash = "sha256:28e33f46f2ce7a41fb9d677a4a2c95327285579b64ca104437c4b9eb1e4174e9", size = 3724368 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/90/a3/285eb1e79dbbd8e9513a3bb1bb2bb3d4c7c22c8a92efb8449baface0b864/jupytext-1.16.4-py3-none-any.whl", hash = "sha256:76989d2690e65667ea6fb411d8056abe7cd0437c07bd774660b83d62acf9490a", size = 153540 },
+]
+
 [[package]]
 name = "keyring"
 version = "25.5.0"
@@ -794,6 +926,27 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/c8/a4/df2bdca5270ca85fd25253049eb6708d4127be2ed0e5c2650217450b59e9/kiwisolver-1.4.7-cp313-cp313-win_arm64.whl", hash = "sha256:76c8094ac20ec259471ac53e774623eb62e6e1f56cd8690c67ce6ce4fcb05650", size = 48530 },
 ]
 
+[[package]]
+name = "markdown"
+version = "3.7"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/54/28/3af612670f82f4c056911fbbbb42760255801b3068c48de792d354ff4472/markdown-3.7.tar.gz", hash = "sha256:2ae2471477cfd02dbbf038d5d9bc226d40def84b4fe2986e49b59b6b472bbed2", size = 357086 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/3f/08/83871f3c50fc983b88547c196d11cf8c3340e37c32d2e9d6152abe2c61f7/Markdown-3.7-py3-none-any.whl", hash = "sha256:7eb6df5690b81a1d7942992c97fad2938e956e79df20cbc6186e9c3a77b1c803", size = 106349 },
+]
+
+[[package]]
+name = "markdown-it-py"
+version = "3.0.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "mdurl" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/38/71/3b932df36c1a044d397a1f92d1cf91ee0a503d91e470cbd670aa66b07ed0/markdown-it-py-3.0.0.tar.gz", hash = "sha256:e3f60a94fa066dc52ec76661e37c851cb232d92f9886b15cb560aaada2df8feb", size = 74596 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/42/d7/1ec15b46af6af88f19b8e5ffea08fa375d433c998b8a7639e76935c14f1f/markdown_it_py-3.0.0-py3-none-any.whl", hash = "sha256:355216845c60bd96232cd8d8c40e8f9765cc86f46880e43a8fd22dc1a1a8cab1", size = 87528 },
+]
+
 [[package]]
 name = "markupsafe"
 version = "3.0.2"
@@ -880,6 +1033,178 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/8f/8e/9ad090d3553c280a8060fbf6e24dc1c0c29704ee7d1c372f0c174aa59285/matplotlib_inline-0.1.7-py3-none-any.whl", hash = "sha256:df192d39a4ff8f21b1895d72e6a13f5fcc5099f00fa84384e0ea28c2cc0653ca", size = 9899 },
 ]
 
+[[package]]
+name = "mdit-py-plugins"
+version = "0.4.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "markdown-it-py" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/19/03/a2ecab526543b152300717cf232bb4bb8605b6edb946c845016fa9c9c9fd/mdit_py_plugins-0.4.2.tar.gz", hash = "sha256:5f2cd1fdb606ddf152d37ec30e46101a60512bc0e5fa1a7002c36647b09e26b5", size = 43542 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a7/f7/7782a043553ee469c1ff49cfa1cdace2d6bf99a1f333cf38676b3ddf30da/mdit_py_plugins-0.4.2-py3-none-any.whl", hash = "sha256:0c673c3f889399a33b95e88d2f0d111b4447bdfea7f237dab2d488f459835636", size = 55316 },
+]
+
+[[package]]
+name = "mdurl"
+version = "0.1.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/d6/54/cfe61301667036ec958cb99bd3efefba235e65cdeb9c84d24a8293ba1d90/mdurl-0.1.2.tar.gz", hash = "sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba", size = 8729 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/b3/38/89ba8ad64ae25be8de66a6d463314cf1eb366222074cfda9ee839c56a4b4/mdurl-0.1.2-py3-none-any.whl", hash = "sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8", size = 9979 },
+]
+
+[[package]]
+name = "mergedeep"
+version = "1.3.4"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/3a/41/580bb4006e3ed0361b8151a01d324fb03f420815446c7def45d02f74c270/mergedeep-1.3.4.tar.gz", hash = "sha256:0096d52e9dad9939c3d975a774666af186eda617e6ca84df4c94dec30004f2a8", size = 4661 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/2c/19/04f9b178c2d8a15b076c8b5140708fa6ffc5601fb6f1e975537072df5b2a/mergedeep-1.3.4-py3-none-any.whl", hash = "sha256:70775750742b25c0d8f36c55aed03d24c3384d17c951b3175d898bd778ef0307", size = 6354 },
+]
+
+[[package]]
+name = "mistune"
+version = "3.0.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/ef/c8/f0173fe3bf85fd891aee2e7bcd8207dfe26c2c683d727c5a6cc3aec7b628/mistune-3.0.2.tar.gz", hash = "sha256:fc7f93ded930c92394ef2cb6f04a8aabab4117a91449e72dcc8dfa646a508be8", size = 90840 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f0/74/c95adcdf032956d9ef6c89a9b8a5152bf73915f8c633f3e3d88d06bd699c/mistune-3.0.2-py3-none-any.whl", hash = "sha256:71481854c30fdbc938963d3605b72501f5c10a9320ecd412c121c163a1c7d205", size = 47958 },
+]
+
+[[package]]
+name = "mkdocs"
+version = "1.6.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "click" },
+    { name = "colorama", marker = "platform_system == 'Windows'" },
+    { name = "ghp-import" },
+    { name = "jinja2" },
+    { name = "markdown" },
+    { name = "markupsafe" },
+    { name = "mergedeep" },
+    { name = "mkdocs-get-deps" },
+    { name = "packaging" },
+    { name = "pathspec" },
+    { name = "pyyaml" },
+    { name = "pyyaml-env-tag" },
+    { name = "watchdog" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/bc/c6/bbd4f061bd16b378247f12953ffcb04786a618ce5e904b8c5a01a0309061/mkdocs-1.6.1.tar.gz", hash = "sha256:7b432f01d928c084353ab39c57282f29f92136665bdd6abf7c1ec8d822ef86f2", size = 3889159 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/22/5b/dbc6a8cddc9cfa9c4971d59fb12bb8d42e161b7e7f8cc89e49137c5b279c/mkdocs-1.6.1-py3-none-any.whl", hash = "sha256:db91759624d1647f3f34aa0c3f327dd2601beae39a366d6e064c03468d35c20e", size = 3864451 },
+]
+
+[[package]]
+name = "mkdocs-autorefs"
+version = "1.2.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "markdown" },
+    { name = "markupsafe" },
+    { name = "mkdocs" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/fb/ae/0f1154c614d6a8b8a36fff084e5b82af3a15f7d2060cf0dcdb1c53297a71/mkdocs_autorefs-1.2.0.tar.gz", hash = "sha256:a86b93abff653521bda71cf3fc5596342b7a23982093915cb74273f67522190f", size = 40262 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/71/26/4d39d52ea2219604053a4d05b98e90d6a335511cc01806436ec4886b1028/mkdocs_autorefs-1.2.0-py3-none-any.whl", hash = "sha256:d588754ae89bd0ced0c70c06f58566a4ee43471eeeee5202427da7de9ef85a2f", size = 16522 },
+]
+
+[[package]]
+name = "mkdocs-get-deps"
+version = "0.2.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "mergedeep" },
+    { name = "platformdirs" },
+    { name = "pyyaml" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/98/f5/ed29cd50067784976f25ed0ed6fcd3c2ce9eb90650aa3b2796ddf7b6870b/mkdocs_get_deps-0.2.0.tar.gz", hash = "sha256:162b3d129c7fad9b19abfdcb9c1458a651628e4b1dea628ac68790fb3061c60c", size = 10239 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/9f/d4/029f984e8d3f3b6b726bd33cafc473b75e9e44c0f7e80a5b29abc466bdea/mkdocs_get_deps-0.2.0-py3-none-any.whl", hash = "sha256:2bf11d0b133e77a0dd036abeeb06dec8775e46efa526dc70667d8863eefc6134", size = 9521 },
+]
+
+[[package]]
+name = "mkdocs-jupyter"
+version = "0.25.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "ipykernel" },
+    { name = "jupytext" },
+    { name = "mkdocs" },
+    { name = "mkdocs-material" },
+    { name = "nbconvert" },
+    { name = "pygments" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/6c/23/6ffb8d2fd2117aa860a04c6fe2510b21bc3c3c085907ffdd851caba53152/mkdocs_jupyter-0.25.1.tar.gz", hash = "sha256:0e9272ff4947e0ec683c92423a4bfb42a26477c103ab1a6ab8277e2dcc8f7afe", size = 1626747 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/08/37/5f1fd5c3f6954b3256f8126275e62af493b96fb6aef6c0dbc4ee326032ad/mkdocs_jupyter-0.25.1-py3-none-any.whl", hash = "sha256:3f679a857609885d322880e72533ef5255561bbfdb13cfee2a1e92ef4d4ad8d8", size = 1456197 },
+]
+
+[[package]]
+name = "mkdocs-material"
+version = "9.5.48"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "babel" },
+    { name = "colorama" },
+    { name = "jinja2" },
+    { name = "markdown" },
+    { name = "mkdocs" },
+    { name = "mkdocs-material-extensions" },
+    { name = "paginate" },
+    { name = "pygments" },
+    { name = "pymdown-extensions" },
+    { name = "regex" },
+    { name = "requests" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/73/e3/925e4c619c03cd538b77d329479f64d75ed9ae35f5d936a19023204de6eb/mkdocs_material-9.5.48.tar.gz", hash = "sha256:a582531e8b34f4c7ed38c29d5c44763053832cf2a32f7409567e0c74749a47db", size = 3936033 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/3e/c2/5cb2482c12d3473c00b6a8b8fe7305613142d418d87871edb83a9eb89981/mkdocs_material-9.5.48-py3-none-any.whl", hash = "sha256:b695c998f4b939ce748adbc0d3bff73fa886a670ece948cf27818fa115dc16f8", size = 8666114 },
+]
+
+[[package]]
+name = "mkdocs-material-extensions"
+version = "1.3.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/79/9b/9b4c96d6593b2a541e1cb8b34899a6d021d208bb357042823d4d2cabdbe7/mkdocs_material_extensions-1.3.1.tar.gz", hash = "sha256:10c9511cea88f568257f960358a467d12b970e1f7b2c0e5fb2bb48cab1928443", size = 11847 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/5b/54/662a4743aa81d9582ee9339d4ffa3c8fd40a4965e033d77b9da9774d3960/mkdocs_material_extensions-1.3.1-py3-none-any.whl", hash = "sha256:adff8b62700b25cb77b53358dad940f3ef973dd6db797907c49e3c2ef3ab4e31", size = 8728 },
+]
+
+[[package]]
+name = "mkdocstrings"
+version = "0.27.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "click" },
+    { name = "jinja2" },
+    { name = "markdown" },
+    { name = "markupsafe" },
+    { name = "mkdocs" },
+    { name = "mkdocs-autorefs" },
+    { name = "platformdirs" },
+    { name = "pymdown-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/e2/5a/5de70538c2cefae7ac3a15b5601e306ef3717290cb2aab11d51cbbc2d1c0/mkdocstrings-0.27.0.tar.gz", hash = "sha256:16adca6d6b0a1f9e0c07ff0b02ced8e16f228a9d65a37c063ec4c14d7b76a657", size = 94830 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/cd/10/4c27c3063c2b3681a4b7942f8dbdeb4fa34fecb2c19b594e7345ebf4f86f/mkdocstrings-0.27.0-py3-none-any.whl", hash = "sha256:6ceaa7ea830770959b55a16203ac63da24badd71325b96af950e59fd37366332", size = 30658 },
+]
+
+[[package]]
+name = "mkdocstrings-python"
+version = "1.12.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "griffe" },
+    { name = "mkdocs-autorefs" },
+    { name = "mkdocstrings" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/23/ec/cb6debe2db77f1ef42b25b21d93b5021474de3037cd82385e586aee72545/mkdocstrings_python-1.12.2.tar.gz", hash = "sha256:7a1760941c0b52a2cd87b960a9e21112ffe52e7df9d0b9583d04d47ed2e186f3", size = 168207 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/5b/c1/ac524e1026d9580cbc654b5d19f5843c8b364a66d30f956372cd09fd2f92/mkdocstrings_python-1.12.2-py3-none-any.whl", hash = "sha256:7f7d40d6db3cb1f5d19dbcd80e3efe4d0ba32b073272c0c0de9de2e604eda62a", size = 111759 },
+]
+
 [[package]]
 name = "ml-dtypes"
 version = "0.4.1"
@@ -938,6 +1263,62 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/ba/eb/988fdc5380e263f3f4ce40dd544720edc2ae5bd8f85c019ccdc6668399e5/narwhals-1.14.2-py3-none-any.whl", hash = "sha256:2e784800b87c9e1ff47984da0046d957320f39b64c08f0e5b1b1a1208694935c", size = 225143 },
 ]
 
+[[package]]
+name = "nbclient"
+version = "0.10.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "jupyter-client" },
+    { name = "jupyter-core" },
+    { name = "nbformat" },
+    { name = "traitlets" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/06/db/25929926860ba8a3f6123d2d0a235e558e0e4be7b46e9db063a7dfefa0a2/nbclient-0.10.1.tar.gz", hash = "sha256:3e93e348ab27e712acd46fccd809139e356eb9a31aab641d1a7991a6eb4e6f68", size = 62273 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/26/1a/ed6d1299b1a00c1af4a033fdee565f533926d819e084caf0d2832f6f87c6/nbclient-0.10.1-py3-none-any.whl", hash = "sha256:949019b9240d66897e442888cfb618f69ef23dc71c01cb5fced8499c2cfc084d", size = 25344 },
+]
+
+[[package]]
+name = "nbconvert"
+version = "7.16.4"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "beautifulsoup4" },
+    { name = "bleach" },
+    { name = "defusedxml" },
+    { name = "jinja2" },
+    { name = "jupyter-core" },
+    { name = "jupyterlab-pygments" },
+    { name = "markupsafe" },
+    { name = "mistune" },
+    { name = "nbclient" },
+    { name = "nbformat" },
+    { name = "packaging" },
+    { name = "pandocfilters" },
+    { name = "pygments" },
+    { name = "tinycss2" },
+    { name = "traitlets" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/af/e8/ba521a033b21132008e520c28ceb818f9f092da5f0261e94e509401b29f9/nbconvert-7.16.4.tar.gz", hash = "sha256:86ca91ba266b0a448dc96fa6c5b9d98affabde2867b363258703536807f9f7f4", size = 854422 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/b8/bb/bb5b6a515d1584aa2fd89965b11db6632e4bdc69495a52374bcc36e56cfa/nbconvert-7.16.4-py3-none-any.whl", hash = "sha256:05873c620fe520b6322bf8a5ad562692343fe3452abda5765c7a34b7d1aa3eb3", size = 257388 },
+]
+
+[[package]]
+name = "nbformat"
+version = "5.10.4"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "fastjsonschema" },
+    { name = "jsonschema" },
+    { name = "jupyter-core" },
+    { name = "traitlets" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/6d/fd/91545e604bc3dad7dca9ed03284086039b294c6b3d75c0d2fa45f9e9caf3/nbformat-5.10.4.tar.gz", hash = "sha256:322168b14f937a5d11362988ecac2a4952d3d8e3a2cbeb2319584631226d5b3a", size = 142749 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a9/82/0340caa499416c78e5d8f5f05947ae4bc3cba53c9f038ab6e9ed964e22f1/nbformat-5.10.4-py3-none-any.whl", hash = "sha256:3b48d6c8fbca4b299bf3982ea7db1af21580e4fec269ad087b9e81588891200b", size = 78454 },
+]
+
 [[package]]
 name = "nest-asyncio"
 version = "1.6.0"
@@ -990,6 +1371,24 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/88/ef/eb23f262cca3c0c4eb7ab1933c3b1f03d021f2c48f54763065b6f0e321be/packaging-24.2-py3-none-any.whl", hash = "sha256:09abb1bccd265c01f4a3aa3f7a7db064b36514d2cba19a2f694fe6150451a759", size = 65451 },
 ]
 
+[[package]]
+name = "paginate"
+version = "0.5.7"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/ec/46/68dde5b6bc00c1296ec6466ab27dddede6aec9af1b99090e1107091b3b84/paginate-0.5.7.tar.gz", hash = "sha256:22bd083ab41e1a8b4f3690544afb2c60c25e5c9a63a30fa2f483f6c60c8e5945", size = 19252 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/90/96/04b8e52da071d28f5e21a805b19cb9390aa17a47462ac87f5e2696b9566d/paginate-0.5.7-py2.py3-none-any.whl", hash = "sha256:b885e2af73abcf01d9559fd5216b57ef722f8c42affbb63942377668e35c7591", size = 13746 },
+]
+
+[[package]]
+name = "pandocfilters"
+version = "1.5.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/70/6f/3dd4940bbe001c06a65f88e36bad298bc7a0de5036115639926b0c5c0458/pandocfilters-1.5.1.tar.gz", hash = "sha256:002b4a555ee4ebc03f8b66307e287fa492e4a77b4ea14d3f934328297bb4939e", size = 8454 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ef/af/4fbc8cab944db5d21b7e2a5b8e9211a03a79852b1157e2c102fcc61ac440/pandocfilters-1.5.1-py2.py3-none-any.whl", hash = "sha256:93be382804a9cdb0a7267585f157e5d1731bbe5545a85b268d6f5fe6232de2bc", size = 8663 },
+]
+
 [[package]]
 name = "parso"
 version = "0.8.4"
@@ -999,6 +1398,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/c6/ac/dac4a63f978e4dcb3c6d3a78c4d8e0192a113d288502a1216950c41b1027/parso-0.8.4-py2.py3-none-any.whl", hash = "sha256:a418670a20291dacd2dddc80c377c5c3791378ee1e8d12bffc35420643d43f18", size = 103650 },
 ]
 
+[[package]]
+name = "pathspec"
+version = "0.12.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/ca/bc/f35b8446f4531a7cb215605d100cd88b7ac6f44ab3fc94870c120ab3adbf/pathspec-0.12.1.tar.gz", hash = "sha256:a482d51503a1ab33b1c67a6c3813a26953dbdc71c31dacaef9a838c4e29f5712", size = 51043 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/cc/20/ff623b09d963f88bfde16306a54e12ee5ea43e9b597108672ff3a408aad6/pathspec-0.12.1-py3-none-any.whl", hash = "sha256:a0d503e138a4c123b27490a4f7beda6a01c6f288df0e4a8b79c7eb0dc7b4cc08", size = 31191 },
+]
+
 [[package]]
 name = "penzai"
 version = "0.2.3"
@@ -1209,6 +1617,19 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/f7/3f/01c8b82017c199075f8f788d0d906b9ffbbc5a47dc9918a945e13d5a2bda/pygments-2.18.0-py3-none-any.whl", hash = "sha256:b8e6aca0523f3ab76fee51799c488e38782ac06eafcf95e7ba832985c8e7b13a", size = 1205513 },
 ]
 
+[[package]]
+name = "pymdown-extensions"
+version = "10.12"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "markdown" },
+    { name = "pyyaml" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/d8/0b/32f05854cfd432e9286bb41a870e0d1a926b72df5f5cdb6dec962b2e369e/pymdown_extensions-10.12.tar.gz", hash = "sha256:b0ee1e0b2bef1071a47891ab17003bfe5bf824a398e13f49f8ed653b699369a7", size = 840790 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/53/32/95a164ddf533bd676cbbe878e36e89b4ade3efde8dd61d0148c90cbbe57e/pymdown_extensions-10.12-py3-none-any.whl", hash = "sha256:49f81412242d3527b8b4967b990df395c89563043bc51a3d2d7d500e52123b77", size = 263448 },
+]
+
 [[package]]
 name = "pyparsing"
 version = "3.2.0"
@@ -1293,6 +1714,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/fa/de/02b54f42487e3d3c6efb3f89428677074ca7bf43aae402517bc7cca949f3/PyYAML-6.0.2-cp313-cp313-win_amd64.whl", hash = "sha256:8388ee1976c416731879ac16da0aff3f63b286ffdd57cdeb95f3f2e085687563", size = 156446 },
 ]
 
+[[package]]
+name = "pyyaml-env-tag"
+version = "0.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pyyaml" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/fb/8e/da1c6c58f751b70f8ceb1eb25bc25d524e8f14fe16edcce3f4e3ba08629c/pyyaml_env_tag-0.1.tar.gz", hash = "sha256:70092675bda14fdec33b31ba77e7543de9ddc88f2e5b99160396572d11525bdb", size = 5631 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/5a/66/bbb1dd374f5c870f59c5bb1db0e18cbe7fa739415a24cbd95b2d1f5ae0c4/pyyaml_env_tag-0.1-py3-none-any.whl", hash = "sha256:af31106dec8a4d68c60207c1886031cbf839b68aa7abccdb19868200532c2069", size = 3911 },
+]
+
 [[package]]
 name = "pyzmq"
 version = "26.2.0"
@@ -1350,6 +1783,44 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/b7/59/2056f61236782a2c86b33906c025d4f4a0b17be0161b63b70fd9e8775d36/referencing-0.35.1-py3-none-any.whl", hash = "sha256:eda6d3234d62814d1c64e305c1331c9a3a6132da475ab6382eaa997b21ee75de", size = 26684 },
 ]
 
+[[package]]
+name = "regex"
+version = "2024.11.6"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/8e/5f/bd69653fbfb76cf8604468d3b4ec4c403197144c7bfe0e6a5fc9e02a07cb/regex-2024.11.6.tar.gz", hash = "sha256:7ab159b063c52a0333c884e4679f8d7a85112ee3078fe3d9004b2dd875585519", size = 399494 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ba/30/9a87ce8336b172cc232a0db89a3af97929d06c11ceaa19d97d84fa90a8f8/regex-2024.11.6-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:52fb28f528778f184f870b7cf8f225f5eef0a8f6e3778529bdd40c7b3920796a", size = 483781 },
+    { url = "https://files.pythonhosted.org/packages/01/e8/00008ad4ff4be8b1844786ba6636035f7ef926db5686e4c0f98093612add/regex-2024.11.6-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:fdd6028445d2460f33136c55eeb1f601ab06d74cb3347132e1c24250187500d9", size = 288455 },
+    { url = "https://files.pythonhosted.org/packages/60/85/cebcc0aff603ea0a201667b203f13ba75d9fc8668fab917ac5b2de3967bc/regex-2024.11.6-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:805e6b60c54bf766b251e94526ebad60b7de0c70f70a4e6210ee2891acb70bf2", size = 284759 },
+    { url = "https://files.pythonhosted.org/packages/94/2b/701a4b0585cb05472a4da28ee28fdfe155f3638f5e1ec92306d924e5faf0/regex-2024.11.6-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b85c2530be953a890eaffde05485238f07029600e8f098cdf1848d414a8b45e4", size = 794976 },
+    { url = "https://files.pythonhosted.org/packages/4b/bf/fa87e563bf5fee75db8915f7352e1887b1249126a1be4813837f5dbec965/regex-2024.11.6-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:bb26437975da7dc36b7efad18aa9dd4ea569d2357ae6b783bf1118dabd9ea577", size = 833077 },
+    { url = "https://files.pythonhosted.org/packages/a1/56/7295e6bad94b047f4d0834e4779491b81216583c00c288252ef625c01d23/regex-2024.11.6-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:abfa5080c374a76a251ba60683242bc17eeb2c9818d0d30117b4486be10c59d3", size = 823160 },
+    { url = "https://files.pythonhosted.org/packages/fb/13/e3b075031a738c9598c51cfbc4c7879e26729c53aa9cca59211c44235314/regex-2024.11.6-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:70b7fa6606c2881c1db9479b0eaa11ed5dfa11c8d60a474ff0e095099f39d98e", size = 796896 },
+    { url = "https://files.pythonhosted.org/packages/24/56/0b3f1b66d592be6efec23a795b37732682520b47c53da5a32c33ed7d84e3/regex-2024.11.6-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:0c32f75920cf99fe6b6c539c399a4a128452eaf1af27f39bce8909c9a3fd8cbe", size = 783997 },
+    { url = "https://files.pythonhosted.org/packages/f9/a1/eb378dada8b91c0e4c5f08ffb56f25fcae47bf52ad18f9b2f33b83e6d498/regex-2024.11.6-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:982e6d21414e78e1f51cf595d7f321dcd14de1f2881c5dc6a6e23bbbbd68435e", size = 781725 },
+    { url = "https://files.pythonhosted.org/packages/83/f2/033e7dec0cfd6dda93390089864732a3409246ffe8b042e9554afa9bff4e/regex-2024.11.6-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:a7c2155f790e2fb448faed6dd241386719802296ec588a8b9051c1f5c481bc29", size = 789481 },
+    { url = "https://files.pythonhosted.org/packages/83/23/15d4552ea28990a74e7696780c438aadd73a20318c47e527b47a4a5a596d/regex-2024.11.6-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:149f5008d286636e48cd0b1dd65018548944e495b0265b45e1bffecce1ef7f39", size = 852896 },
+    { url = "https://files.pythonhosted.org/packages/e3/39/ed4416bc90deedbfdada2568b2cb0bc1fdb98efe11f5378d9892b2a88f8f/regex-2024.11.6-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:e5364a4502efca094731680e80009632ad6624084aff9a23ce8c8c6820de3e51", size = 860138 },
+    { url = "https://files.pythonhosted.org/packages/93/2d/dd56bb76bd8e95bbce684326302f287455b56242a4f9c61f1bc76e28360e/regex-2024.11.6-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:0a86e7eeca091c09e021db8eb72d54751e527fa47b8d5787caf96d9831bd02ad", size = 787692 },
+    { url = "https://files.pythonhosted.org/packages/0b/55/31877a249ab7a5156758246b9c59539abbeba22461b7d8adc9e8475ff73e/regex-2024.11.6-cp312-cp312-win32.whl", hash = "sha256:32f9a4c643baad4efa81d549c2aadefaeba12249b2adc5af541759237eee1c54", size = 262135 },
+    { url = "https://files.pythonhosted.org/packages/38/ec/ad2d7de49a600cdb8dd78434a1aeffe28b9d6fc42eb36afab4a27ad23384/regex-2024.11.6-cp312-cp312-win_amd64.whl", hash = "sha256:a93c194e2df18f7d264092dc8539b8ffb86b45b899ab976aa15d48214138e81b", size = 273567 },
+    { url = "https://files.pythonhosted.org/packages/90/73/bcb0e36614601016552fa9344544a3a2ae1809dc1401b100eab02e772e1f/regex-2024.11.6-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:a6ba92c0bcdf96cbf43a12c717eae4bc98325ca3730f6b130ffa2e3c3c723d84", size = 483525 },
+    { url = "https://files.pythonhosted.org/packages/0f/3f/f1a082a46b31e25291d830b369b6b0c5576a6f7fb89d3053a354c24b8a83/regex-2024.11.6-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:525eab0b789891ac3be914d36893bdf972d483fe66551f79d3e27146191a37d4", size = 288324 },
+    { url = "https://files.pythonhosted.org/packages/09/c9/4e68181a4a652fb3ef5099e077faf4fd2a694ea6e0f806a7737aff9e758a/regex-2024.11.6-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:086a27a0b4ca227941700e0b31425e7a28ef1ae8e5e05a33826e17e47fbfdba0", size = 284617 },
+    { url = "https://files.pythonhosted.org/packages/fc/fd/37868b75eaf63843165f1d2122ca6cb94bfc0271e4428cf58c0616786dce/regex-2024.11.6-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bde01f35767c4a7899b7eb6e823b125a64de314a8ee9791367c9a34d56af18d0", size = 795023 },
+    { url = "https://files.pythonhosted.org/packages/c4/7c/d4cd9c528502a3dedb5c13c146e7a7a539a3853dc20209c8e75d9ba9d1b2/regex-2024.11.6-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:b583904576650166b3d920d2bcce13971f6f9e9a396c673187f49811b2769dc7", size = 833072 },
+    { url = "https://files.pythonhosted.org/packages/4f/db/46f563a08f969159c5a0f0e722260568425363bea43bb7ae370becb66a67/regex-2024.11.6-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:1c4de13f06a0d54fa0d5ab1b7138bfa0d883220965a29616e3ea61b35d5f5fc7", size = 823130 },
+    { url = "https://files.pythonhosted.org/packages/db/60/1eeca2074f5b87df394fccaa432ae3fc06c9c9bfa97c5051aed70e6e00c2/regex-2024.11.6-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3cde6e9f2580eb1665965ce9bf17ff4952f34f5b126beb509fee8f4e994f143c", size = 796857 },
+    { url = "https://files.pythonhosted.org/packages/10/db/ac718a08fcee981554d2f7bb8402f1faa7e868c1345c16ab1ebec54b0d7b/regex-2024.11.6-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:0d7f453dca13f40a02b79636a339c5b62b670141e63efd511d3f8f73fba162b3", size = 784006 },
+    { url = "https://files.pythonhosted.org/packages/c2/41/7da3fe70216cea93144bf12da2b87367590bcf07db97604edeea55dac9ad/regex-2024.11.6-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:59dfe1ed21aea057a65c6b586afd2a945de04fc7db3de0a6e3ed5397ad491b07", size = 781650 },
+    { url = "https://files.pythonhosted.org/packages/a7/d5/880921ee4eec393a4752e6ab9f0fe28009435417c3102fc413f3fe81c4e5/regex-2024.11.6-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:b97c1e0bd37c5cd7902e65f410779d39eeda155800b65fc4d04cc432efa9bc6e", size = 789545 },
+    { url = "https://files.pythonhosted.org/packages/dc/96/53770115e507081122beca8899ab7f5ae28ae790bfcc82b5e38976df6a77/regex-2024.11.6-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:f9d1e379028e0fc2ae3654bac3cbbef81bf3fd571272a42d56c24007979bafb6", size = 853045 },
+    { url = "https://files.pythonhosted.org/packages/31/d3/1372add5251cc2d44b451bd94f43b2ec78e15a6e82bff6a290ef9fd8f00a/regex-2024.11.6-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:13291b39131e2d002a7940fb176e120bec5145f3aeb7621be6534e46251912c4", size = 860182 },
+    { url = "https://files.pythonhosted.org/packages/ed/e3/c446a64984ea9f69982ba1a69d4658d5014bc7a0ea468a07e1a1265db6e2/regex-2024.11.6-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:4f51f88c126370dcec4908576c5a627220da6c09d0bff31cfa89f2523843316d", size = 787733 },
+    { url = "https://files.pythonhosted.org/packages/2b/f1/e40c8373e3480e4f29f2692bd21b3e05f296d3afebc7e5dcf21b9756ca1c/regex-2024.11.6-cp313-cp313-win32.whl", hash = "sha256:63b13cfd72e9601125027202cad74995ab26921d8cd935c25f09c630436348ff", size = 262122 },
+    { url = "https://files.pythonhosted.org/packages/45/94/bc295babb3062a731f52621cdc992d123111282e291abaf23faa413443ea/regex-2024.11.6-cp313-cp313-win_amd64.whl", hash = "sha256:2b3361af3198667e99927da8b84c1b010752fa4b1115ee30beaa332cabc3ef1a", size = 273545 },
+]
+
 [[package]]
 name = "requests"
 version = "2.32.3"
@@ -1460,6 +1931,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/d9/5a/e7c31adbe875f2abbb91bd84cf2dc52d792b5a01506781dbcf25c91daf11/six-1.16.0-py2.py3-none-any.whl", hash = "sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254", size = 11053 },
 ]
 
+[[package]]
+name = "soupsieve"
+version = "2.6"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/d7/ce/fbaeed4f9fb8b2daa961f90591662df6a86c1abf25c548329a86920aedfb/soupsieve-2.6.tar.gz", hash = "sha256:e2e68417777af359ec65daac1057404a3c8a5455bb8abc36f1a9866ab1a51abb", size = 101569 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d1/c2/fe97d779f3ef3b15f05c94a2f1e3d21732574ed441687474db9d342a7315/soupsieve-2.6-py3-none-any.whl", hash = "sha256:e72c4ff06e4fb6e4b5a9f0f55fe6e81514581fca1515028625d0f299c602ccc9", size = 36186 },
+]
+
 [[package]]
 name = "stack-data"
 version = "0.6.3"
@@ -1491,6 +1971,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/1e/90/4e8c686f2e691f48e40e16a539c61a6e9880743733d8c4dc3f275d12268e/tensorflow_probability-0.23.0-py2.py3-none-any.whl", hash = "sha256:dda5cacfe50cb19ecd96f3ce81e6ff8680d84213bcfe94ca0aaf6e5f51c88061", size = 6915575 },
 ]
 
+[[package]]
+name = "tinycss2"
+version = "1.4.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "webencodings" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/7a/fd/7a5ee21fd08ff70d3d33a5781c255cbe779659bd03278feb98b19ee550f4/tinycss2-1.4.0.tar.gz", hash = "sha256:10c0972f6fc0fbee87c3edb76549357415e94548c1ae10ebccdea16fb404a9b7", size = 87085 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e6/34/ebdc18bae6aa14fbee1a08b63c015c72b64868ff7dae68808ab500c492e2/tinycss2-1.4.0-py3-none-any.whl", hash = "sha256:3a49cf47b7675da0b15d0c6e1df8df4ebd96e9394bb905a5775adb0d884c5289", size = 26610 },
+]
+
 [[package]]
 name = "tornado"
 version = "6.4.2"
@@ -1560,6 +2052,30 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/ce/d9/5f4c13cecde62396b0d3fe530a50ccea91e7dfc1ccf0e09c228841bb5ba8/urllib3-2.2.3-py3-none-any.whl", hash = "sha256:ca899ca043dcb1bafa3e262d73aa25c465bfb49e0bd9dd5d59f1d0acba2f8fac", size = 126338 },
 ]
 
+[[package]]
+name = "watchdog"
+version = "6.0.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/db/7d/7f3d619e951c88ed75c6037b246ddcf2d322812ee8ea189be89511721d54/watchdog-6.0.0.tar.gz", hash = "sha256:9ddf7c82fda3ae8e24decda1338ede66e1c99883db93711d8fb941eaa2d8c282", size = 131220 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/39/ea/3930d07dafc9e286ed356a679aa02d777c06e9bfd1164fa7c19c288a5483/watchdog-6.0.0-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:bdd4e6f14b8b18c334febb9c4425a878a2ac20efd1e0b231978e7b150f92a948", size = 96471 },
+    { url = "https://files.pythonhosted.org/packages/12/87/48361531f70b1f87928b045df868a9fd4e253d9ae087fa4cf3f7113be363/watchdog-6.0.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:c7c15dda13c4eb00d6fb6fc508b3c0ed88b9d5d374056b239c4ad1611125c860", size = 88449 },
+    { url = "https://files.pythonhosted.org/packages/5b/7e/8f322f5e600812e6f9a31b75d242631068ca8f4ef0582dd3ae6e72daecc8/watchdog-6.0.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:6f10cb2d5902447c7d0da897e2c6768bca89174d0c6e1e30abec5421af97a5b0", size = 89054 },
+    { url = "https://files.pythonhosted.org/packages/68/98/b0345cabdce2041a01293ba483333582891a3bd5769b08eceb0d406056ef/watchdog-6.0.0-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:490ab2ef84f11129844c23fb14ecf30ef3d8a6abafd3754a6f75ca1e6654136c", size = 96480 },
+    { url = "https://files.pythonhosted.org/packages/85/83/cdf13902c626b28eedef7ec4f10745c52aad8a8fe7eb04ed7b1f111ca20e/watchdog-6.0.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:76aae96b00ae814b181bb25b1b98076d5fc84e8a53cd8885a318b42b6d3a5134", size = 88451 },
+    { url = "https://files.pythonhosted.org/packages/fe/c4/225c87bae08c8b9ec99030cd48ae9c4eca050a59bf5c2255853e18c87b50/watchdog-6.0.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:a175f755fc2279e0b7312c0035d52e27211a5bc39719dd529625b1930917345b", size = 89057 },
+    { url = "https://files.pythonhosted.org/packages/a9/c7/ca4bf3e518cb57a686b2feb4f55a1892fd9a3dd13f470fca14e00f80ea36/watchdog-6.0.0-py3-none-manylinux2014_aarch64.whl", hash = "sha256:7607498efa04a3542ae3e05e64da8202e58159aa1fa4acddf7678d34a35d4f13", size = 79079 },
+    { url = "https://files.pythonhosted.org/packages/5c/51/d46dc9332f9a647593c947b4b88e2381c8dfc0942d15b8edc0310fa4abb1/watchdog-6.0.0-py3-none-manylinux2014_armv7l.whl", hash = "sha256:9041567ee8953024c83343288ccc458fd0a2d811d6a0fd68c4c22609e3490379", size = 79078 },
+    { url = "https://files.pythonhosted.org/packages/d4/57/04edbf5e169cd318d5f07b4766fee38e825d64b6913ca157ca32d1a42267/watchdog-6.0.0-py3-none-manylinux2014_i686.whl", hash = "sha256:82dc3e3143c7e38ec49d61af98d6558288c415eac98486a5c581726e0737c00e", size = 79076 },
+    { url = "https://files.pythonhosted.org/packages/ab/cc/da8422b300e13cb187d2203f20b9253e91058aaf7db65b74142013478e66/watchdog-6.0.0-py3-none-manylinux2014_ppc64.whl", hash = "sha256:212ac9b8bf1161dc91bd09c048048a95ca3a4c4f5e5d4a7d1b1a7d5752a7f96f", size = 79077 },
+    { url = "https://files.pythonhosted.org/packages/2c/3b/b8964e04ae1a025c44ba8e4291f86e97fac443bca31de8bd98d3263d2fcf/watchdog-6.0.0-py3-none-manylinux2014_ppc64le.whl", hash = "sha256:e3df4cbb9a450c6d49318f6d14f4bbc80d763fa587ba46ec86f99f9e6876bb26", size = 79078 },
+    { url = "https://files.pythonhosted.org/packages/62/ae/a696eb424bedff7407801c257d4b1afda455fe40821a2be430e173660e81/watchdog-6.0.0-py3-none-manylinux2014_s390x.whl", hash = "sha256:2cce7cfc2008eb51feb6aab51251fd79b85d9894e98ba847408f662b3395ca3c", size = 79077 },
+    { url = "https://files.pythonhosted.org/packages/b5/e8/dbf020b4d98251a9860752a094d09a65e1b436ad181faf929983f697048f/watchdog-6.0.0-py3-none-manylinux2014_x86_64.whl", hash = "sha256:20ffe5b202af80ab4266dcd3e91aae72bf2da48c0d33bdb15c66658e685e94e2", size = 79078 },
+    { url = "https://files.pythonhosted.org/packages/07/f6/d0e5b343768e8bcb4cda79f0f2f55051bf26177ecd5651f84c07567461cf/watchdog-6.0.0-py3-none-win32.whl", hash = "sha256:07df1fdd701c5d4c8e55ef6cf55b8f0120fe1aef7ef39a1c6fc6bc2e606d517a", size = 79065 },
+    { url = "https://files.pythonhosted.org/packages/db/d9/c495884c6e548fce18a8f40568ff120bc3a4b7b99813081c8ac0c936fa64/watchdog-6.0.0-py3-none-win_amd64.whl", hash = "sha256:cbafb470cf848d93b5d013e2ecb245d4aa1c8fd0504e863ccefa32445359d680", size = 79070 },
+    { url = "https://files.pythonhosted.org/packages/33/e8/e40370e6d74ddba47f002a32919d91310d6074130fe4e17dabcafc15cbf1/watchdog-6.0.0-py3-none-win_ia64.whl", hash = "sha256:a1914259fa9e1454315171103c6a30961236f508b9b623eae470268bbcc6a22f", size = 79067 },
+]
+
 [[package]]
 name = "wcwidth"
 version = "0.2.13"
@@ -1569,6 +2085,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/fd/84/fd2ba7aafacbad3c4201d395674fc6348826569da3c0937e75505ead3528/wcwidth-0.2.13-py2.py3-none-any.whl", hash = "sha256:3da69048e4540d84af32131829ff948f1e022c1c6bdb8d6102117aac784f6859", size = 34166 },
 ]
 
+[[package]]
+name = "webencodings"
+version = "0.5.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/0b/02/ae6ceac1baeda530866a85075641cec12989bd8d31af6d5ab4a3e8c92f47/webencodings-0.5.1.tar.gz", hash = "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923", size = 9721 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f4/24/2a3e3df732393fed8b3ebf2ec078f05546de641fe1b667ee316ec1dcf3b7/webencodings-0.5.1-py2.py3-none-any.whl", hash = "sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78", size = 11774 },
+]
+
 [[package]]
 name = "wheel"
 version = "0.45.1"

From d1c8630406b8ae19c6d1ca19fb56cbe61bd3f683 Mon Sep 17 00:00:00 2001
From: Ian Limarta <ian.limarta@gmail.com>
Date: Tue, 17 Dec 2024 14:10:33 -0600
Subject: [PATCH 4/8] Tutorial

---
 docs/tutorials/getting_started.md |   2 -
 docs/tutorials/test.py            |  59 ++++++-
 examples/clean.ipynb              | 207 +++++++++++++++++++++++
 examples/vector.ipynb             | 264 ++++++++++++++++++++++++++++++
 mkdocs.yml                        |   3 +-
 5 files changed, 531 insertions(+), 4 deletions(-)
 create mode 100644 examples/clean.ipynb
 create mode 100644 examples/vector.ipynb

diff --git a/docs/tutorials/getting_started.md b/docs/tutorials/getting_started.md
index 41ce592..2de0565 100644
--- a/docs/tutorials/getting_started.md
+++ b/docs/tutorials/getting_started.md
@@ -5,8 +5,6 @@ This tutorial introduces Dirichlet Process Mixture Models and explores how to cl
 # Dataset
 First we need a dataset. In this tutorial, we will synthetically generate points on on the 2D plane and form small clusters that we expect to later detect during inference. We will generate data using a [Gaussian Mixture Model](https://en.wikipedia.org/wiki/Mixture_model#Gaussian_mixture_model) in pure JAX and later feed in this dataset to GenJAXMix.
 
-
-
 # Generative Model
 
 # Inference 
diff --git a/docs/tutorials/test.py b/docs/tutorials/test.py
index ed7dfc3..ec88e95 100644
--- a/docs/tutorials/test.py
+++ b/docs/tutorials/test.py
@@ -1 +1,58 @@
-import jax
\ No newline at end of file
+# %% [markdown]
+# # Test
+
+# %% [markdown]
+# This tutorial introduces Dirichlet Process Mixture Models and explores how to cluster using a simple example.
+
+# %% [markdown]
+# # Dataset
+# First we need a dataset. In this tutorial, we will synthetically generate points on on the 2D plane and form small clusters that we expect to later detect during inference. We will generate data using a [Gaussian Mixture Model](https://en.wikipedia.org/wiki/Mixture_model#Gaussian_mixture_model) in pure JAX and later feed in this dataset to GenJAXMix.
+#
+
+# %%
+import jax
+import matplotlib.pyplot as plt
+
+key = jax.random.key(0)
+K_max = 5
+N = 1000
+
+# Generate cluster proportions
+betas = jax.random.beta(key, 1.0, 1.0, shape=(K_max, ))
+beta_not = jax.lax.cumprod(1 - betas[:-1])
+beta_not = jax.numpy.concatenate([jax.numpy.array([1.0]), beta_not])
+weights = betas * beta_not 
+
+# Generate cluster centers
+cluster_centers = jax.random.normal(key, (K_max, 2)) 
+
+
+# %% [markdown]
+# We follow [the stick-breaking process](https://en.wikipedia.org/wiki/Dirichlet_process#The_stick-breaking_process) description for Dirichlet Process Mixture Models. First we generate the stick lengths which represents the proportion of data points expected to belong to each cluster. We then generate the cluster centers - here we use sample the clusters using normal distributions.
+
+# %%
+
+# Sample cluster assignments
+cluster_assignments = jax.random.categorical(key, jax.numpy.log(weights), shape=(N,))
+
+# Generate data points
+data_points = cluster_centers[cluster_assignments] + jax.random.normal(key, (N, 2))/5
+
+# Plot the clusters
+plt.figure(figsize=(8, 6))
+plt.scatter(data_points[:, 0], data_points[:, 1], c=cluster_assignments, cmap='viridis', alpha=0.6)
+plt.scatter(cluster_centers[:, 0], cluster_centers[:, 1], c='red', marker='x', s=100, label='Cluster Centers')
+plt.title('Generated Data Points and Cluster Centers')
+plt.xlabel('X-axis')
+plt.ylabel('Y-axis')
+plt.legend()
+plt.show()
+
+# %% [markdown]
+# Here we generated 100 data points for five clusters. 
+
+# %% [markdown]
+# # Model
+# We will now define a Dirichlet Process Mixture Model using GenJAXMix to cluster this dataset.
+
+# %%
\ No newline at end of file
diff --git a/examples/clean.ipynb b/examples/clean.ipynb
new file mode 100644
index 0000000..bc55c65
--- /dev/null
+++ b/examples/clean.ipynb
@@ -0,0 +1,207 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Platform 'METAL' is experimental and not all JAX functionality may be correctly supported!\n",
+      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
+      "W0000 00:00:1732727905.549339 19028456 mps_client.cc:510] WARNING: JAX Apple GPU support is experimental and not all JAX functionality is correctly supported!\n",
+      "I0000 00:00:1732727905.563482 19028456 service.cc:145] XLA service 0x109fdd760 initialized for platform METAL (this does not guarantee that XLA will be used). Devices:\n",
+      "I0000 00:00:1732727905.563494 19028456 service.cc:153]   StreamExecutor device (0): Metal, <undefined>\n",
+      "I0000 00:00:1732727905.564831 19028456 mps_client.cc:406] Using Simple allocator.\n",
+      "I0000 00:00:1732727905.564845 19028456 mps_client.cc:384] XLA backend will use up to 11452858368 bytes on device 0 for SimpleAllocator.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Metal device set to: Apple M1 Pro\n",
+      "\n",
+      "systemMemory: 16.00 GB\n",
+      "maxCacheSize: 5.33 GB\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import jax\n",
+    "# import genjaxmix.dpmm as dpmm\n",
+    "from genjaxmix import generate\n",
+    "from genjax import pretty\n",
+    "pretty()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<script> (()=>{ if (customElements.get('treescope-container') === undefined) { class TreescopeContainer extends HTMLElement { constructor() { super(); this.attachShadow({mode: \"open\"}); this.defns = {}; this.state = {}; } } customElements.define(\"treescope-container\", TreescopeContainer); } if (customElements.get('treescope-run-here') === undefined) { class RunHere extends HTMLElement { constructor() { super() } connectedCallback() { const run = child => { const fn = new Function(child.textContent); child.textContent = \"\"; fn.call(this); this.remove(); }; const child = this.querySelector(\"script\"); if (child) { run(child); } else { new MutationObserver(()=>{ run(this.querySelector(\"script\")); }).observe(this, {childList: true}); } } } customElements.define(\"treescope-run-here\", RunHere); } })(); </script> <treescope-container class=\"treescope_out_42c735c1877f46aaab37a550c4002951\" ></treescope-container> <treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_42c735c1877f46aaab37a550c4002951\")) .filter((elt) => !elt.dataset.setup) )[0]; root.dataset.setup = 1; const msg = document.createElement(\"span\"); msg.style = \"color: #cccccc; font-family: monospace;\"; msg.textContent = \"(Loading...)\"; root.state.loadingMsg = msg; root.shadowRoot.appendChild(msg); root.state.chain = new Promise((resolve, reject) => { const observer = new IntersectionObserver((entries) => { for (const entry of entries) { if (entry.isIntersecting) { resolve(); observer.disconnect(); return; } } }, {rootMargin: \"1000px\"}); window.setTimeout(() => { observer.observe(root); }, 0); }); root.state.deferring = false; const _insertNode = (node) => { for (let oldScript of node.querySelectorAll(\"script\")) { let newScript = document.createElement(\"script\"); newScript.type = oldScript.type; newScript.textContent = oldScript.textContent; oldScript.parentNode.replaceChild(newScript, oldScript); } if (root.state.loadingMsg) { root.state.loadingMsg.remove(); root.state.loadingMsg = null; } root.shadowRoot.appendChild(node); }; root.defns.insertContent = ((contentNode, compressed) => { if (compressed) { root.state.deferring = true; } if (root.state.deferring) { root.state.chain = (async () => { await root.state.chain; if (compressed) { const encoded = contentNode.textContent; const blob = new Blob([ Uint8Array.from(atob(encoded), (m) => m.codePointAt(0)) ]); const reader = blob.stream().pipeThrough( new DecompressionStream(\"deflate\") ).pipeThrough( new TextDecoderStream(\"utf-8\") ).getReader(); const parts = []; while (true) { const step = await reader.read(); if (step.done) { break; } parts.push(step.value); } const tpl = document.createElement('template'); tpl.innerHTML = parts.join(\"\"); _insertNode(tpl.content); } else { _insertNode(contentNode.content); } })(); } else { _insertNode(contentNode.content); } }); </script></treescope-run-here><div style=\"display:none\"> <script type=\"application/octet-stream\" >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</script> <treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_42c735c1877f46aaab37a550c4002951\")) .filter((elt) => !elt.dataset['step0']) )[0]; root.dataset['step0'] = 1; root.defns.insertContent( this.parentNode.querySelector('script[type=\"application/octet-stream\"]'), true ); this.parentNode.remove(); </script></treescope-run-here> </div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"display:none\"> <script type=\"application/octet-stream\" >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</script> <treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_42c735c1877f46aaab37a550c4002951\")) .filter((elt) => !elt.dataset['step1']) )[0]; root.dataset['step1'] = 1; root.defns.insertContent( this.parentNode.querySelector('script[type=\"application/octet-stream\"]'), true ); this.parentNode.remove(); </script></treescope-run-here> </div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_42c735c1877f46aaab37a550c4002951\")) .filter((elt) => !elt.dataset.stolen) )[0]; root.dataset.stolen = 1; this.parentNode.replaceChild(root, this); </script></treescope-run-here>"
+      ],
+      "text/plain": [
+       "StaticTrace(\n",
+       "  gen_fn=StaticGenerativeFunction(\n",
+       "    source=Closure(dyn_args=(), fn=<function generate.<locals>.dpmm at 0x128690a40>),\n",
+       "  ),\n",
+       "  args=(\n",
+       "    <jax.Array(1., dtype=float32, weak_type=True)>,\n",
+       "    <jax.Array(0., dtype=float32, weak_type=True)>,\n",
+       "    <jax.Array(1., dtype=float32, weak_type=True)>,\n",
+       "    <jax.Array(3., dtype=float32, weak_type=True)>,\n",
+       "    <jax.Array(0.5, dtype=float32, weak_type=True)>,\n",
+       "  ),\n",
+       "  retval=(\n",
+       "    <jax.Array int32(7,) [≥0, ≤3] zero:3 nonzero:4>,\n",
+       "    <jax.Array float32(7,) ≈-0.24 ±0.45 [≥-0.88, ≤0.3] nonzero:7>,\n",
+       "    <jax.Array int32(7,) [≥3, ≤6] nonzero:7>,\n",
+       "  ),\n",
+       "  addresses=AddressVisitor(visited=[('pi',), ('hyperparameters',), ('assignments',)]),\n",
+       "  subtraces=[StaticTrace(gen_fn=StaticGenerativeFunction(source=Closure(dyn_args=(), fn=<function gem at 0x1286907c0>)), args=(<jax.Array(1., dtype=float32, weak_type=True)>,), retval=<jax.Array float32(5,) ≈-2.4 ±1.6 [≥-5.3, ≤-0.71] nonzero:5>, addresses=AddressVisitor(visited=[('pi',)]), subtraces=[DistributionTrace(gen_fn=ExactDensityFromCallables(sampler=Closure(dyn_args=(), fn=<function tfp_distribution.<locals>.sampler at 0x128529580>), logpdf_evaluator=Closure(dyn_args=(), fn=<function tfp_distribution.<locals>.logpdf at 0x128529760>)), args=(<jax.Array float32(5,) ≈1.0 ±0.0 [≥1.0, ≤1.0] nonzero:5>, <jax.Array float32(5,) ≈1.0 ±0.0 [≥1.0, ≤1.0] nonzero:5>), value=<jax.Array float32(5,) ≈0.49 ±0.25 [≥0.21, ≤0.95] nonzero:5>, score=<jax.Array(-2.3841858e-06, dtype=float32)>)]), StaticTrace(gen_fn=StaticGenerativeFunction(source=Closure(dyn_args=(), fn=<function hyperparameters at 0x10eb54d60>)), args=(<jax.Array(0., dtype=float32, weak_type=True)>, <jax.Array(1., dtype=float32, weak_type=True)>, <jax.Array(3., dtype=float32, weak_type=True)>, <jax.Array(0.5, dtype=float32, weak_type=True)>), retval=(<jax.Array float32(5,) ≈-0.013 ±0.26 [≥-0.48, ≤0.28] nonzero:5>, <jax.Array float32(5,) ≈0.43 ±0.088 [≥0.35, ≤0.55] nonzero:5>, <jax.Array float32(5, 7) ≈-2.3 ±0.97 [≥-5.0, ≤-0.47] nonzero:35>), addresses=AddressVisitor(visited=[('sigma',), ('mu',), ('logp',)]), subtraces=[DistributionTrace(gen_fn=ExactDensityFromCallables(sampler=Closure(dyn_args=(), fn=<function tfp_distribution.<locals>.sampler at 0x12852aa20>), logpdf_evaluator=Closure(dyn_args=(), fn=<function tfp_distribution.<locals>.logpdf at 0x12852aac0>)), args=(<jax.Array float32(5,) ≈3.0 ±0.0 [≥3.0, ≤3.0] nonzero:5>, <jax.Array float32(5,) ≈0.5 ±0.0 [≥0.5, ≤0.5] nonzero:5>), value=<jax.Array float32(5,) ≈0.19 ±0.079 [≥0.12, ≤0.3] nonzero:5>, score=<jax.Array(5.493408, dtype=float32)>), DistributionTrace(gen_fn=ExactDensityFromCallables(sampler=Closure(dyn_args=(), fn=<function tfp_distribution.<locals>.sampler at 0x12852b880>), logpdf_evaluator=Closure(dyn_args=(), fn=<function tfp_distribution.<locals>.logpdf at 0x12852b920>)), args=(<jax.Array float32(5,) ≈0.0 ±0.0 [≥0.0, ≤0.0] zero:5>, <jax.Array float32(5,) ≈0.43 ±0.088 [≥0.35, ≤0.55] nonzero:5>), value=<jax.Array float32(5,) ≈-0.013 ±0.26 [≥-0.48, ≤0.28] nonzero:5>, score=<jax.Array(-1.4883857, dtype=float32)>), DistributionTrace(gen_fn=ExactDensityFromCallables(sampler=Closure(dyn_args=(), fn=<function tfp_distribution.<locals>.sampler at 0x12852a200>), logpdf_evaluator=Closure(dyn_args=(), fn=<function tfp_distribution.<locals>.logpdf at 0x12852a2a0>)), args=(<jax.Array float32(5, 7) ≈1.0 ±0.0 [≥1.0, ≤1.0] nonzero:35>,), value=<jax.Array float32(5, 7) ≈0.14 ±0.12 [≥0.007, ≤0.62] nonzero:35>, score=<jax.Array(32.896248, dtype=float32)>)]), DimapTrace(gen_fn=DimapCombinator(inner=VmapCombinator(gen_fn=DimapCombinator(inner=StaticGenerativeFunction(source=Closure(dyn_args=(), fn=<function cluster at 0x101247100>)), argument_mapping=<function RepeatCombinator.<locals>.<lambda> at 0x128690900>, retval_mapping=<function contramap.<locals>.<lambda> at 0x128690ae0>, info=None), in_axes=(0, None)), argument_mapping=<function RepeatCombinator.<locals>.<lambda> at 0x128690c20>, retval_mapping=<function contramap.<locals>.<lambda> at 0x128690d60>, info=None), inner=VmapTrace(gen_fn=VmapCombinator(gen_fn=DimapCombinator(inner=StaticGenerativeFunction(source=Closure(dyn_args=(), fn=<function cluster at 0x101247100>)), argument_mapping=<function RepeatCombinator.<locals>.<lambda> at 0x128690900>, retval_mapping=<function contramap.<locals>.<lambda> at 0x128690ae0>, info=None), in_axes=(0, None)), inner=DimapTrace(gen_fn=DimapCombinator(inner=StaticGenerativeFunction(source=Closure(dyn_args=(), fn=<function cluster at 0x101247100>)), argument_mapping=<function RepeatCombinator.<locals>.<lambda> at 0x128690900>, retval_mapping=<function contramap.<locals>.<lambda> at 0x128690ae0>, info=None), inner=StaticTrace(gen_fn=StaticGenerativeFunction(source=Closure(dyn_args=(), fn=<function cluster at 0x101247100>)), args=(<jax.Array float32(7, 5) ≈-2.4 ±1.6 [≥-5.3, ≤-0.71] nonzero:35>, <jax.Array float32(7, 5) ≈-0.013 ±0.26 [≥-0.48, ≤0.28] nonzero:35>, <jax.Array float32(7, 5) ≈0.43 ±0.088 [≥0.35, ≤0.55] nonzero:35>, <jax.Array float32(7, 5, 7) ≈-2.3 ±0.97 [≥-5.0, ≤-0.47] nonzero:245>), retval=(<jax.Array int32(7,) [≥0, ≤3] zero:3 nonzero:4>, <jax.Array float32(7,) ≈-0.24 ±0.45 [≥-0.88, ≤0.3] nonzero:7>, <jax.Array int32(7,) [≥3, ≤6] nonzero:7>), addresses=AddressVisitor(visited=[('c',), ('y1',), ('y2',)]), subtraces=[DistributionTrace(gen_fn=ExactDensityFromCallables(sampler=Closure(dyn_args=(), fn=<function tfp_distribution.<locals>.sampler at 0x12852bd80>), logpdf_evaluator=Closure(dyn_args=(), fn=<function tfp_distribution.<locals>.logpdf at 0x12852be20>)), args=(<jax.Array float32(7, 5) ≈-2.4 ±1.6 [≥-5.3, ≤-0.71] nonzero:35>,), value=<jax.Array int32(7,) [≥0, ≤3] zero:3 nonzero:4>, score=<jax.Array float32(7,) ≈-1.2 ±0.39 [≥-1.5, ≤-0.7] nonzero:7>), DistributionTrace(gen_fn=ExactDensityFromCallables(sampler=Closure(dyn_args=(), fn=<function tfp_distribution.<locals>.sampler at 0x12852b880>), logpdf_evaluator=Closure(dyn_args=(), fn=<function tfp_distribution.<locals>.logpdf at 0x12852b920>)), args=(<jax.Array float32(7,) ≈-0.27 ±0.24 [≥-0.48, ≤-0.00083] nonzero:7>, <jax.Array float32(7,) ≈0.44 ±0.1 [≥0.35, ≤0.55] nonzero:7>), value=<jax.Array float32(7,) ≈-0.24 ±0.45 [≥-0.88, ≤0.3] nonzero:7>, score=<jax.Array float32(7,) ≈-0.3 ±0.21 [≥-0.53, ≤0.038] nonzero:7>), DistributionTrace(gen_fn=ExactDensityFromCallables(sampler=Closure(dyn_args=(), fn=<function tfp_distribution.<locals>.sampler at 0x12852bd80>), logpdf_evaluator=Closure(dyn_args=(), fn=<function tfp_distribution.<locals>.logpdf at 0x12852be20>)), args=(<jax.Array float32(7, 7) ≈-2.5 ±1.2 [≥-5.0, ≤-0.47] nonzero:49>,), value=<jax.Array int32(7,) [≥3, ≤6] nonzero:7>, score=<jax.Array float32(7,) ≈-1.2 ±0.7 [≥-2.3, ≤-0.47] nonzero:7>)]), args=(<jax.Array float32(7,) ≈0.0 ±0.0 [≥0.0, ≤0.0] zero:7>, (<jax.Array float32(7, 5) ≈-2.4 ±1.6 [≥-5.3, ≤-0.71] nonzero:35>, <jax.Array float32(7, 5) ≈-0.013 ±0.26 [≥-0.48, ≤0.28] nonzero:35>, <jax.Array float32(7, 5) ≈0.43 ±0.088 [≥0.35, ≤0.55] nonzero:35>, <jax.Array float32(7, 5, 7) ≈-2.3 ±0.97 [≥-5.0, ≤-0.47] nonzero:245>)), retval=(<jax.Array int32(7,) [≥0, ≤3] zero:3 nonzero:4>, <jax.Array float32(7,) ≈-0.24 ±0.45 [≥-0.88, ≤0.3] nonzero:7>, <jax.Array int32(7,) [≥3, ≤6] nonzero:7>)), args=(<jax.Array float32(7,) ≈0.0 ±0.0 [≥0.0, ≤0.0] zero:7>, (<jax.Array float32(5,) ≈-2.4 ±1.6 [≥-5.3, ≤-0.71] nonzero:5>, <jax.Array float32(5,) ≈-0.013 ±0.26 [≥-0.48, ≤0.28] nonzero:5>, <jax.Array float32(5,) ≈0.43 ±0.088 [≥0.35, ≤0.55] nonzero:5>, <jax.Array float32(5, 7) ≈-2.3 ±0.97 [≥-5.0, ≤-0.47] nonzero:35>)), score=<jax.Array(-18.766577, dtype=float32)>, chm=Static({'c': Choice(v=<jax.Array int32(7,) [≥0, ≤3] zero:3 nonzero:4>), 'y1': Choice(v=<jax.Array float32(7,) ≈-0.24 ±0.45 [≥-0.88, ≤0.3] nonzero:7>), 'y2': Choice(v=<jax.Array int32(7,) [≥3, ≤6] nonzero:7>)}), dim_length=7), args=(<jax.Array float32(5,) ≈-2.4 ±1.6 [≥-5.3, ≤-0.71] nonzero:5>, <jax.Array float32(5,) ≈-0.013 ±0.26 [≥-0.48, ≤0.28] nonzero:5>, <jax.Array float32(5,) ≈0.43 ±0.088 [≥0.35, ≤0.55] nonzero:5>, <jax.Array float32(5, 7) ≈-2.3 ±0.97 [≥-5.0, ≤-0.47] nonzero:35>), retval=(<jax.Array int32(7,) [≥0, ≤3] zero:3 nonzero:4>, <jax.Array float32(7,) ≈-0.24 ±0.45 [≥-0.88, ≤0.3] nonzero:7>, <jax.Array int32(7,) [≥3, ≤6] nonzero:7>))],\n",
+       ")"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "key = jax.random.key(0)\n",
+    "\n",
+    "model = generate(N_max=7)\n",
+    "tr = jax.jit(model.simulate)(key, (1.0, 0.0, 1.0, 3.0, 0.5))\n",
+    "tr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Old parameters\n",
+      "mu  [-0.6985804  -0.8248212   0.21795563  0.83772683 -1.0604821 ]\n",
+      "sigma  [0.3464867  0.3496993  0.340931   0.37010777 0.25367144]\n",
+      "logp  [[0.09438226 0.00282767 0.04096948 0.08485078 0.0489018  0.49663323\n",
+      "  0.23143476]\n",
+      " [0.13750365 0.13743183 0.1123658  0.38792682 0.02242342 0.04836479\n",
+      "  0.15398356]\n",
+      " [0.162733   0.0838028  0.10103215 0.16631661 0.1136003  0.26927957\n",
+      "  0.10323555]\n",
+      " [0.07585612 0.11168033 0.42487857 0.1220006  0.21683493 0.04375059\n",
+      "  0.00499873]\n",
+      " [0.2314116  0.0582075  0.02395316 0.00416281 0.02321914 0.21533836\n",
+      "  0.4437075 ]]\n",
+      "\n",
+      "New parameters\n",
+      "mu  [-0.6111581  -0.4638838   0.41751742  3.9061658  -0.45953447]\n",
+      "sigma  [0.35883906 0.35544455 0.40168008 5.6444116  1.1374482 ]\n",
+      "logp  [[0.02781614 0.00530343 0.06577533 0.06104895 0.03928433 0.46402544\n",
+      "  0.33674636]\n",
+      " [0.         0.50572026 0.36900312 0.12527657 0.         0.\n",
+      "  0.        ]\n",
+      " [0.07929692 0.17245011 0.         0.         0.15229306 0.39758736\n",
+      "  0.19837251]\n",
+      " [1.         0.         0.         0.         0.         0.\n",
+      "  0.        ]\n",
+      " [0.         0.         0.         1.         0.         0.\n",
+      "  0.        ]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from genjaxmix.rejuvenation import gibbs_move, propose_parameters\n",
+    "from genjax._src.core.interpreters.incremental import Diff\n",
+    "\n",
+    "key = jax.random.key(3)\n",
+    "model = generate(100)\n",
+    "model_args = (1.0, 0.0, 1.0, 3.0, 0.5)\n",
+    "tr = model.simulate(key, model_args)\n",
+    "obs = tr.get_choices()(\"assignments\")\n",
+    "\n",
+    "# print(\"Data \", obs[:, \"y2\"])\n",
+    "# print(\"c = \", obs[:,\"c\"])\n",
+    "print(\"Old parameters\")\n",
+    "print(\"mu \", tr.get_choices()[\"hyperparameters\", \"mu\"])\n",
+    "print(\"sigma \", tr.get_choices()[\"hyperparameters\", \"sigma\"])\n",
+    "print(\"logp \", tr.get_choices()[\"hyperparameters\", \"logp\"])\n",
+    "key, *subkeys = jax.random.split(key, 5)\n",
+    "tr_new = gibbs_move(model, propose_parameters, model_args, tr, obs, subkeys[0])\n",
+    "tr_new = gibbs_move(model, propose_parameters, model_args, tr_new, obs, subkeys[1])\n",
+    "tr_new = gibbs_move(model, propose_parameters, model_args, tr_new, obs, subkeys[2])\n",
+    "tr_new = gibbs_move(model, propose_parameters, model_args, tr_new, obs, subkeys[3])\n",
+    "# print(tr_new)\n",
+    "print()\n",
+    "print(\"New parameters\")\n",
+    "print(\"mu \", tr_new.get_choices()[\"hyperparameters\", \"mu\"])\n",
+    "print(\"sigma \", tr_new.get_choices()[\"hyperparameters\", \"sigma\"])\n",
+    "print(\"logp \", tr_new.get_choices()[\"hyperparameters\", \"logp\"])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/vector.ipynb b/examples/vector.ipynb
new file mode 100644
index 0000000..a4977cb
--- /dev/null
+++ b/examples/vector.ipynb
@@ -0,0 +1,264 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Platform 'METAL' is experimental and not all JAX functionality may be correctly supported!\n",
+      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
+      "W0000 00:00:1732740992.297821 19079572 mps_client.cc:510] WARNING: JAX Apple GPU support is experimental and not all JAX functionality is correctly supported!\n",
+      "I0000 00:00:1732740992.315939 19079572 service.cc:145] XLA service 0x13f419b70 initialized for platform METAL (this does not guarantee that XLA will be used). Devices:\n",
+      "I0000 00:00:1732740992.315961 19079572 service.cc:153]   StreamExecutor device (0): Metal, <undefined>\n",
+      "I0000 00:00:1732740992.317166 19079572 mps_client.cc:406] Using Simple allocator.\n",
+      "I0000 00:00:1732740992.317172 19079572 mps_client.cc:384] XLA backend will use up to 11452858368 bytes on device 0 for SimpleAllocator.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Metal device set to: Apple M1 Pro\n",
+      "\n",
+      "systemMemory: 16.00 GB\n",
+      "maxCacheSize: 5.33 GB\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import jax\n",
+    "import jax.numpy as jnp\n",
+    "import genjaxmix.vectorized as vectorized\n",
+    "from genjax import pretty\n",
+    "pretty()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<script> (()=>{ if (customElements.get('treescope-container') === undefined) { class TreescopeContainer extends HTMLElement { constructor() { super(); this.attachShadow({mode: \"open\"}); this.defns = {}; this.state = {}; } } customElements.define(\"treescope-container\", TreescopeContainer); } if (customElements.get('treescope-run-here') === undefined) { class RunHere extends HTMLElement { constructor() { super() } connectedCallback() { const run = child => { const fn = new Function(child.textContent); child.textContent = \"\"; fn.call(this); this.remove(); }; const child = this.querySelector(\"script\"); if (child) { run(child); } else { new MutationObserver(()=>{ run(this.querySelector(\"script\")); }).observe(this, {childList: true}); } } } customElements.define(\"treescope-run-here\", RunHere); } })(); </script> <treescope-container class=\"treescope_out_40cc33079a4240aeacd5b16fedaad2e7\" ></treescope-container> <treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_40cc33079a4240aeacd5b16fedaad2e7\")) .filter((elt) => !elt.dataset.setup) )[0]; root.dataset.setup = 1; const msg = document.createElement(\"span\"); msg.style = \"color: #cccccc; font-family: monospace;\"; msg.textContent = \"(Loading...)\"; root.state.loadingMsg = msg; root.shadowRoot.appendChild(msg); root.state.chain = new Promise((resolve, reject) => { const observer = new IntersectionObserver((entries) => { for (const entry of entries) { if (entry.isIntersecting) { resolve(); observer.disconnect(); return; } } }, {rootMargin: \"1000px\"}); window.setTimeout(() => { observer.observe(root); }, 0); }); root.state.deferring = false; const _insertNode = (node) => { for (let oldScript of node.querySelectorAll(\"script\")) { let newScript = document.createElement(\"script\"); newScript.type = oldScript.type; newScript.textContent = oldScript.textContent; oldScript.parentNode.replaceChild(newScript, oldScript); } if (root.state.loadingMsg) { root.state.loadingMsg.remove(); root.state.loadingMsg = null; } root.shadowRoot.appendChild(node); }; root.defns.insertContent = ((contentNode, compressed) => { if (compressed) { root.state.deferring = true; } if (root.state.deferring) { root.state.chain = (async () => { await root.state.chain; if (compressed) { const encoded = contentNode.textContent; const blob = new Blob([ Uint8Array.from(atob(encoded), (m) => m.codePointAt(0)) ]); const reader = blob.stream().pipeThrough( new DecompressionStream(\"deflate\") ).pipeThrough( new TextDecoderStream(\"utf-8\") ).getReader(); const parts = []; while (true) { const step = await reader.read(); if (step.done) { break; } parts.push(step.value); } const tpl = document.createElement('template'); tpl.innerHTML = parts.join(\"\"); _insertNode(tpl.content); } else { _insertNode(contentNode.content); } })(); } else { _insertNode(contentNode.content); } }); </script></treescope-run-here><div style=\"display:none\"> <script type=\"application/octet-stream\" >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</script> <treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_40cc33079a4240aeacd5b16fedaad2e7\")) .filter((elt) => !elt.dataset['step0']) )[0]; root.dataset['step0'] = 1; root.defns.insertContent( this.parentNode.querySelector('script[type=\"application/octet-stream\"]'), true ); this.parentNode.remove(); </script></treescope-run-here> </div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"display:none\"> <script type=\"application/octet-stream\" >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</script> <treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_40cc33079a4240aeacd5b16fedaad2e7\")) .filter((elt) => !elt.dataset['step1']) )[0]; root.dataset['step1'] = 1; root.defns.insertContent( this.parentNode.querySelector('script[type=\"application/octet-stream\"]'), true ); this.parentNode.remove(); </script></treescope-run-here> </div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_40cc33079a4240aeacd5b16fedaad2e7\")) .filter((elt) => !elt.dataset.stolen) )[0]; root.dataset.stolen = 1; this.parentNode.replaceChild(root, this); </script></treescope-run-here>"
+      ],
+      "text/plain": [
+       "StaticTrace(\n",
+       "  gen_fn=StaticGenerativeFunction(\n",
+       "    source=Closure(dyn_args=(), fn=<function generate.<locals>.dpmm at 0x33b771c60>),\n",
+       "  ),\n",
+       "  args=(\n",
+       "    <jax.Array(1., dtype=float32, weak_type=True)>,\n",
+       "    <jax.Array(0., dtype=float32, weak_type=True)>,\n",
+       "    <jax.Array(3., dtype=float32, weak_type=True)>,\n",
+       "    <jax.Array(3., dtype=float32, weak_type=True)>,\n",
+       "    <jax.Array(0.5, dtype=float32, weak_type=True)>,\n",
+       "  ),\n",
+       "  retval=(\n",
+       "    <jax.Array int32(1000,) [≥0, ≤4] zero:505 nonzero:495>,\n",
+       "    <jax.Array float32(1000, 2) ≈-0.84 ±1.7 [≥-5.6, ≤1.6] nonzero:2_000>,\n",
+       "  ),\n",
+       "  addresses=AddressVisitor(\n",
+       "    visited=[('pi',), ('hyperparameters',), ('assignments',)],\n",
+       "  ),\n",
+       "  subtraces=[StaticTrace(gen_fn=StaticGenerativeFunction(source=Closure(dyn_args=(), fn=<function gem at 0x13f39cc20>)), args=(<jax.Array(1., dtype=float32, weak_type=True)>,), retval=<jax.Array float32(5,) ≈-2.4 ±1.6 [≥-5.3, ≤-0.71] nonzero:5>, addresses=AddressVisitor(visited=[('pi',)]), subtraces=[DistributionTrace(gen_fn=ExactDensityFromCallables(sampler=Closure(dyn_args=(), fn=<function tfp_distribution.<locals>.sampler at 0x3069256c0>), logpdf_evaluator=Closure(dyn_args=(), fn=<function tfp_distribution.<locals>.logpdf at 0x3069258a0>)), args=(<jax.Array float32(5,) ≈1.0 ±0.0 [≥1.0, ≤1.0] nonzero:5>, <jax.Array float32(5,) ≈1.0 ±0.0 [≥1.0, ≤1.0] nonzero:5>), value=<jax.Array float32(5,) ≈0.49 ±0.25 [≥0.21, ≤0.95] nonzero:5>, score=<jax.Array(-2.3841858e-06, dtype=float32)>)]), StaticTrace(gen_fn=StaticGenerativeFunction(source=Closure(dyn_args=(), fn=<function hyperparameters at 0x12f40ce00>)), args=(<jax.Array(0., dtype=float32, weak_type=True)>, <jax.Array(3., dtype=float32, weak_type=True)>, <jax.Array(3., dtype=float32, weak_type=True)>, <jax.Array(0.5, dtype=float32, weak_type=True)>, <jax.Array(1., dtype=float32, weak_type=True)>), retval=(<jax.Array float32(5, 2) ≈-0.68 ±1.4 [≥-3.4, ≤0.58] nonzero:10>, <jax.Array float32(5, 2) ≈0.47 ±0.12 [≥0.28, ≤0.68] nonzero:10>), addresses=AddressVisitor(visited=[('sigma',), ('mu',)]), subtraces=[DistributionTrace(gen_fn=ExactDensityFromCallables(sampler=Closure(dyn_args=(), fn=<function tfp_distribution.<locals>.sampler at 0x306926b60>), logpdf_evaluator=Closure(dyn_args=(), fn=<function tfp_distribution.<locals>.logpdf at 0x306926c00>)), args=(<jax.Array float32(5, 2) ≈3.0 ±0.0 [≥3.0, ≤3.0] nonzero:10>, <jax.Array float32(5, 2) ≈0.5 ±0.0 [≥0.5, ≤0.5] nonzero:10>), value=<jax.Array float32(5, 2) ≈0.24 ±0.11 [≥0.081, ≤0.46] nonzero:10>, score=<jax.Array(7.434048, dtype=float32)>), DistributionTrace(gen_fn=ExactDensityFromCallables(sampler=Closure(dyn_args=(), fn=<function tfp_distribution.<locals>.sampler at 0x3069279c0>), logpdf_evaluator=Closure(dyn_args=(), fn=<function tfp_distribution.<locals>.logpdf at 0x306927a60>)), args=(<jax.Array float32(5, 2) ≈0.0 ±0.0 [≥0.0, ≤0.0] zero:10>, <jax.Array float32(5, 2) ≈1.4 ±0.35 [≥0.85, ≤2.0] nonzero:10>), value=<jax.Array float32(5, 2) ≈-0.68 ±1.4 [≥-3.4, ≤0.58] nonzero:10>, score=<jax.Array(-16.965593, dtype=float32)>)]), DimapTrace(gen_fn=DimapCombinator(inner=VmapCombinator(gen_fn=DimapCombinator(inner=StaticGenerativeFunction(source=Closure(dyn_args=(), fn=<function cluster at 0x13f39ccc0>)), argument_mapping=<function RepeatCombinator.<locals>.<lambda> at 0x319a47560>, retval_mapping=<function contramap.<locals>.<lambda> at 0x32d1e5440>, info=None), in_axes=(0, None)), argument_mapping=<function RepeatCombinator.<locals>.<lambda> at 0x3338dd760>, retval_mapping=<function contramap.<locals>.<lambda> at 0x317c437e0>, info=None), inner=VmapTrace(gen_fn=VmapCombinator(gen_fn=DimapCombinator(inner=StaticGenerativeFunction(source=Closure(dyn_args=(), fn=<function cluster at 0x13f39ccc0>)), argument_mapping=<function RepeatCombinator.<locals>.<lambda> at 0x319a47560>, retval_mapping=<function contramap.<locals>.<lambda> at 0x32d1e5440>, info=None), in_axes=(0, None)), inner=DimapTrace(gen_fn=DimapCombinator(inner=StaticGenerativeFunction(source=Closure(dyn_args=(), fn=<function cluster at 0x13f39ccc0>)), argument_mapping=<function RepeatCombinator.<locals>.<lambda> at 0x319a47560>, retval_mapping=<function contramap.<locals>.<lambda> at 0x32d1e5440>, info=None), inner=StaticTrace(gen_fn=StaticGenerativeFunction(source=Closure(dyn_args=(), fn=<function cluster at 0x13f39ccc0>)), args=(<jax.Array float32(1000, 5) ≈-2.4 ±1.6 [≥-5.3, ≤-0.71] nonzero:5_000>, <jax.Array float32(1000, 5, 2) ≈-0.68 ±1.4 [≥-3.4, ≤0.58] nonzero:10_000>, <jax.Array float32(1000, 5, 2) ≈0.47 ±0.12 [≥0.28, ≤0.68] nonzero:10_000>), retval=(<jax.Array int32(1000,) [≥0, ≤4] zero:505 nonzero:495>, <jax.Array float32(1000, 2) ≈-0.84 ±1.7 [≥-5.6, ≤1.6] nonzero:2_000>), addresses=AddressVisitor(visited=[('c',), ('y1',)]), subtraces=[DistributionTrace(gen_fn=ExactDensityFromCallables(sampler=Closure(dyn_args=(), fn=<function tfp_distribution.<locals>.sampler at 0x306927ec0>), logpdf_evaluator=Closure(dyn_args=(), fn=<function tfp_distribution.<locals>.logpdf at 0x306927f60>)), args=(<jax.Array float32(1000, 5) ≈-2.4 ±1.6 [≥-5.3, ≤-0.71] nonzero:5_000>,), value=<jax.Array int32(1000,) [≥0, ≤4] zero:505 nonzero:495>, score=<jax.Array float32(1000,) ≈-1.2 ±0.57 [≥-5.3, ≤-0.7] nonzero:1_000>), DistributionTrace(gen_fn=ExactDensityFromCallables(sampler=Closure(dyn_args=(), fn=<function tfp_distribution.<locals>.sampler at 0x3069279c0>), logpdf_evaluator=Closure(dyn_args=(), fn=<function tfp_distribution.<locals>.logpdf at 0x306927a60>)), args=(<jax.Array float32(1000, 2) ≈-0.86 ±1.5 [≥-3.4, ≤0.58] nonzero:2_000>, <jax.Array float32(1000, 2) ≈0.5 ±0.14 [≥0.28, ≤0.68] nonzero:2_000>), value=<jax.Array float32(1000, 2) ≈-0.84 ±1.7 [≥-5.6, ≤1.6] nonzero:2_000>, score=<jax.Array float32(1000,) ≈-1.4 ±1.1 [≥-8.8, ≤0.13] nonzero:1_000>)]), args=(<jax.Array float32(1000,) ≈0.0 ±0.0 [≥0.0, ≤0.0] zero:1_000>, (<jax.Array float32(1000, 5) ≈-2.4 ±1.6 [≥-5.3, ≤-0.71] nonzero:5_000>, <jax.Array float32(1000, 5, 2) ≈-0.68 ±1.4 [≥-3.4, ≤0.58] nonzero:10_000>, <jax.Array float32(1000, 5, 2) ≈0.47 ±0.12 [≥0.28, ≤0.68] nonzero:10_000>)), retval=(<jax.Array int32(1000,) [≥0, ≤4] zero:505 nonzero:495>, <jax.Array float32(1000, 2) ≈-0.84 ±1.7 [≥-5.6, ≤1.6] nonzero:2_000>)), args=(<jax.Array float32(1000,) ≈0.0 ±0.0 [≥0.0, ≤0.0] zero:1_000>, (<jax.Array float32(5,) ≈-2.4 ±1.6 [≥-5.3, ≤-0.71] nonzero:5>, <jax.Array float32(5, 2) ≈-0.68 ±1.4 [≥-3.4, ≤0.58] nonzero:10>, <jax.Array float32(5, 2) ≈0.47 ±0.12 [≥0.28, ≤0.68] nonzero:10>)), score=<jax.Array(-2602.4907, dtype=float32)>, chm=Static({'c': Choice(v=<jax.Array int32(1000,) [≥0, ≤4] zero:505 nonzero:495>), 'y1': Choice(v=<jax.Array float32(1000, 2) ≈-0.84 ±1.7 [≥-5.6, ≤1.6] nonzero:2_000>)}), dim_length=1000), args=(<jax.Array float32(5,) ≈-2.4 ±1.6 [≥-5.3, ≤-0.71] nonzero:5>, <jax.Array float32(5, 2) ≈-0.68 ±1.4 [≥-3.4, ≤0.58] nonzero:10>, <jax.Array float32(5, 2) ≈0.47 ±0.12 [≥0.28, ≤0.68] nonzero:10>), retval=(<jax.Array int32(1000,) [≥0, ≤4] zero:505 nonzero:495>, <jax.Array float32(1000, 2) ≈-0.84 ±1.7 [≥-5.6, ≤1.6] nonzero:2_000>))],\n",
+       ")"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "key = jax.random.key(0)\n",
+    "\n",
+    "model = vectorized.generate(N_max=1000)\n",
+    "tr_gt = jax.jit(model.simulate)(key, (1.0, 0.0, 3.0, 3.0, 0.5))\n",
+    "tr_gt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "obs = tr_gt.get_choices()\n",
+    "c = obs[\"assignments\", \"c\"]\n",
+    "y1 = obs[\"assignments\", \"y1\"]\n",
+    "mu = obs[\"hyperparameters\", \"mu\"]\n",
+    "\n",
+    "sigma = jnp.sqrt(obs[\"hyperparameters\", \"sigma\"])\n",
+    "\n",
+    "# Create a 2D plot\n",
+    "plt.figure(figsize=(10, 8))\n",
+    "\n",
+    "# Plot each cluster\n",
+    "for cluster in np.unique(c):\n",
+    "    cluster_points = y1[c == cluster]\n",
+    "    cluster_mu = mu[cluster]\n",
+    "    cluster_sigma = sigma[cluster]\n",
+    "\n",
+    "    # Plot the points\n",
+    "    plt.scatter(cluster_points[:, 0], cluster_points[:, 1], label=f'Cluster {cluster}')\n",
+    "\n",
+    "    # Plot the mean\n",
+    "    plt.scatter(cluster_mu[0], cluster_mu[1], color='black', marker='x')\n",
+    "\n",
+    "    # Plot the variance as an ellipse\n",
+    "    ellipse = matplotlib.patches.Ellipse(cluster_mu, 2 * cluster_sigma[0], 2 * cluster_sigma[1], edgecolor='black', facecolor='none')\n",
+    "    plt.gca().add_patch(ellipse)\n",
+    "\n",
+    "plt.xlabel('X')\n",
+    "plt.ylabel('Y')\n",
+    "plt.title('Clusters with Mean and Variance')\n",
+    "plt.legend()\n",
+    "\n",
+    "# Save the plot\n",
+    "plt.savefig('clusters_plot.png')\n",
+    "plt.close()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from genjaxmix.rejuvenation import gibbs_move\n",
+    "from genjaxmix.vectorized_rejuvenation import propose_parameters\n",
+    "from genjax._src.core.interpreters.incremental import Diff\n",
+    "\n",
+    "key = jax.random.key(31415)\n",
+    "model_args = (1.0, 0.0, 1.0, 3.0, 0.5)\n",
+    "obs = tr_gt.get_choices()(\"assignments\")\n",
+    "\n",
+    "gibbs_jitted = jax.jit(gibbs_move)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from genjax import ChoiceMapBuilder as C\n",
+    "key = jax.random.key(313341)\n",
+    "key, subkey = jax.random.split(key)\n",
+    "constraint = C[\"assignments\", \"c\"].set(obs[:, \"c\"]) ^ C[\"assignments\", \"y1\"].set(obs[:, \"y1\"])\n",
+    "\n",
+    "tr,_  = model.importance(subkey, constraint, model_args)\n",
+    "for t in range(10):\n",
+    "    key, subkey = jax.random.split(key)\n",
+    "    tr = gibbs_jitted(model, propose_parameters, model_args, tr, obs, subkey)\n",
+    "\n",
+    "# print(tr.get_choices()[\"assignments\", :, \"c\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "c = obs[:, \"c\"]\n",
+    "y1 = obs[:, \"y1\"]\n",
+    "\n",
+    "new_chm = tr.get_choices()\n",
+    "mu = new_chm[\"hyperparameters\", \"mu\"]\n",
+    "sigma = jnp.sqrt(new_chm[\"hyperparameters\", \"sigma\"])\n",
+    "\n",
+    "# Create a 2D plot\n",
+    "plt.figure(figsize=(10, 8))\n",
+    "\n",
+    "# Plot each cluster\n",
+    "for cluster in np.unique(c):\n",
+    "    cluster_points = y1[c == cluster]\n",
+    "    cluster_mu = mu[cluster]\n",
+    "    cluster_sigma = sigma[cluster]\n",
+    "\n",
+    "    # Plot the points\n",
+    "    plt.scatter(cluster_points[:, 0], cluster_points[:, 1], label=f'Cluster {cluster}')\n",
+    "\n",
+    "    # Plot the mean\n",
+    "    plt.scatter(cluster_mu[0], cluster_mu[1], color='black', marker='x')\n",
+    "\n",
+    "    # Plot the variance as an ellipse\n",
+    "    ellipse = matplotlib.patches.Ellipse(cluster_mu, 2 * cluster_sigma[0], 2 * cluster_sigma[1], edgecolor='black', facecolor='none')\n",
+    "    plt.gca().add_patch(ellipse)\n",
+    "\n",
+    "plt.xlabel('X')\n",
+    "plt.ylabel('Y')\n",
+    "plt.title('Clusters with Mean and Variance')\n",
+    "plt.legend()\n",
+    "\n",
+    "# Save the plot\n",
+    "plt.savefig('clusters_plot_inferred.png')\n",
+    "plt.close()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/mkdocs.yml b/mkdocs.yml
index c4bc3c3..acea292 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -11,7 +11,8 @@ theme:
 #   - toc
 
 plugins:
-  - mkdocs-jupyter
+  - mkdocs-jupyter:
+      execute: true
 
 nav: 
   - Home: index.md

From e86006f25dd03b8a7655ed353057fc9edab95176 Mon Sep 17 00:00:00 2001
From: Ian Limarta <ian.limarta@gmail.com>
Date: Mon, 6 Jan 2025 12:59:10 -0600
Subject: [PATCH 5/8] Push docs

---
 .gitignore                  |   3 +
 docs/api.md                 |   0
 docs/reference/api.md       |   1 +
 docs/reference/conjugacy.md |   1 +
 docs/reference/inference.md |   1 +
 mkdocs.yml                  |  15 ++-
 src/genjaxmix/__init__.py   |  14 ++-
 src/genjaxmix/_smc.py       | 231 ++++++++++++++++++++++++++++++++++++
 src/genjaxmix/conjugacy.py  |  22 +++-
 src/genjaxmix/dpmm.py       |  53 ++++++---
 src/genjaxmix/smc.py        |  60 +++++++++-
 11 files changed, 367 insertions(+), 34 deletions(-)
 delete mode 100644 docs/api.md
 create mode 100644 docs/reference/api.md
 create mode 100644 docs/reference/conjugacy.md
 create mode 100644 docs/reference/inference.md
 create mode 100644 src/genjaxmix/_smc.py

diff --git a/.gitignore b/.gitignore
index c3912f7..5950b5c 100644
--- a/.gitignore
+++ b/.gitignore
@@ -12,3 +12,6 @@ wheels/
 jax_cache/
 
 .DS_Store
+
+site/
+slides/
\ No newline at end of file
diff --git a/docs/api.md b/docs/api.md
deleted file mode 100644
index e69de29..0000000
diff --git a/docs/reference/api.md b/docs/reference/api.md
new file mode 100644
index 0000000..19b9b96
--- /dev/null
+++ b/docs/reference/api.md
@@ -0,0 +1 @@
+::: src.genjaxmix.dpmm
\ No newline at end of file
diff --git a/docs/reference/conjugacy.md b/docs/reference/conjugacy.md
new file mode 100644
index 0000000..01cbc72
--- /dev/null
+++ b/docs/reference/conjugacy.md
@@ -0,0 +1 @@
+::: src.genjaxmix.conjugacy
\ No newline at end of file
diff --git a/docs/reference/inference.md b/docs/reference/inference.md
new file mode 100644
index 0000000..64e638f
--- /dev/null
+++ b/docs/reference/inference.md
@@ -0,0 +1 @@
+::: src.genjaxmix.smc
\ No newline at end of file
diff --git a/mkdocs.yml b/mkdocs.yml
index acea292..b1e48e3 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -13,12 +13,19 @@ theme:
 plugins:
   - mkdocs-jupyter:
       execute: true
+  - mkdocstrings:
+      handlers:
+        python:
+          options:
+            show_source: false
+
 
 nav: 
   - Home: index.md
   - Tutorials: 
-    - Getting Started: tutorials/getting_started.md
-    - Analyzing Heart Diesease Patients: tutorials/heart_disease.md
-    - Test: tutorials/test.py
-  - Library Reference: api.md
+    - Getting Started: tutorials/test.py
+  - Library Reference: 
+    - API: reference/api.md
+    - Conjugacy: reference/conjugacy.md
+    - Inference: reference/inference.md
   - For Developers: dev.md
\ No newline at end of file
diff --git a/src/genjaxmix/__init__.py b/src/genjaxmix/__init__.py
index 376bc60..f90ce71 100644
--- a/src/genjaxmix/__init__.py
+++ b/src/genjaxmix/__init__.py
@@ -1,5 +1,11 @@
-from .dpmm import generate
+"""Run Dirichlet Process Mixture Model inference using GenJAX.
 
-__all__ = [
-    "generate"
-]
\ No newline at end of file
+Modules exported by this package:
+
+- `dpmm`
+"""
+# from .dpmm import generate
+
+# __all__ = [
+#     "generate"
+# ]
\ No newline at end of file
diff --git a/src/genjaxmix/_smc.py b/src/genjaxmix/_smc.py
new file mode 100644
index 0000000..f889687
--- /dev/null
+++ b/src/genjaxmix/_smc.py
@@ -0,0 +1,231 @@
+import jax.numpy as jnp
+import jax
+import numpy as np
+from plum import dispatch
+from jaxtyping import Array, Integer
+from genjaxmix.distributions import (Dirichlet, MixtureModel,
+    posterior, sample, logpdf, Normal, Categorical, Mixed, Cluster, Trace)
+from functools import partial
+
+@partial(jax.jit, static_argnames=['gibbs_iters', 'max_clusters', 'n_steps'])
+def smc(key, trace, data_test, n_steps, data, gibbs_iters, max_clusters):
+    smc_keys = jax.random.split(key, n_steps)
+
+    def wrap_step(trace, n):
+        key = smc_keys[n]
+        keys = jax.random.split(key, 3)
+        new_cluster = step(data=data, trace=trace, gibbs_iters=gibbs_iters, 
+            max_clusters=max_clusters, key=keys[0], K=n+2)
+        split_trace = Trace(
+            gem=trace.gem,
+            g=trace.g,
+            cluster=new_cluster
+        )
+
+        rejuvenated_cluster = rejuvenate(keys[1], data, split_trace, gibbs_iters, max_clusters)
+        rejuvenated_trace = Trace(
+            gem=split_trace.gem,
+            g=trace.g,
+            cluster=rejuvenated_cluster
+        )
+
+        mixture_model = MixtureModel(
+            pi=rejuvenated_trace.cluster.pi/jnp.sum(rejuvenated_trace.cluster.pi), 
+            f=rejuvenated_trace.cluster.f[:max_clusters])
+        logprobs = jax.vmap(logpdf, in_axes=(None, 0))(mixture_model, data_test)
+        sum_logprobs = jnp.sum(logprobs)
+
+        return rejuvenated_trace, (rejuvenated_trace, sum_logprobs)
+
+    carry, (trace, sum_logprobs) = jax.lax.scan(wrap_step, trace, jnp.arange(n_steps))
+    return trace, sum_logprobs
+
+
+def rejuvenate(key, data, trace, gibbs_iters, max_clusters):
+    extended_pi = jnp.concatenate((trace.cluster.pi, jnp.zeros(max_clusters)))
+    log_likelihood_mask = jnp.where(extended_pi == 0, -jnp.inf, 0)
+
+    partial_gibbs_step = partial(gibbs_step, 
+        alpha=trace.gem.alpha, g=trace.g, data=data, 
+        log_likelihood_mask=log_likelihood_mask, max_clusters=max_clusters,
+        rejuvenation=True)
+
+    keys = jax.random.split(key, gibbs_iters)
+    _, q_split_trace = jax.lax.scan(partial_gibbs_step, trace.cluster.c, keys)
+
+    cluster = q_split_trace[-1]
+    cluster = Cluster(cluster.c, jnp.sum(trace.cluster.pi) * cluster.pi[:max_clusters], cluster.f)
+
+    return cluster
+
+@partial(jax.jit, static_argnames=['gibbs_iters', 'max_clusters'])
+def step(data, gibbs_iters, key, K, trace, max_clusters):
+    q_split_trace = q_split(data, gibbs_iters, max_clusters, key, trace.cluster.c, trace.gem.alpha, trace.g)
+
+    cluster_weights = get_weights(trace, K, data, q_split_trace, max_clusters)
+
+    logprob_pi0 = logpdf(trace.gem, jnp.sort(trace.cluster.pi, descending=True), K-1)
+
+    weights = jnp.zeros(max_clusters + 1)
+    weights = weights.at[1:].set(cluster_weights - logprob_pi0)
+    weights = weights.at[0].set(-jnp.inf)  # temp, don't stop
+    k = jax.random.categorical(key, weights)
+
+    new_cluster = jax.lax.cond(k==0, 
+        lambda cluster0, cluster1, k, K, max_clusters: trace.cluster, 
+        lambda  cluster0, cluster1, k, K, max_clusters: split_cluster(cluster0, cluster1, k, K, max_clusters),
+        trace.cluster, q_split_trace[-1], k-1, K, max_clusters)
+
+    return new_cluster
+
+def get_weights(trace, K, data, q_split_trace, max_clusters):
+    # for each cluster, get the pi score
+    pi_split = jax.vmap(make_pi, in_axes=(None, 0, None, None))(
+        trace.cluster.pi, jnp.arange(max_clusters), q_split_trace.pi[-1], max_clusters)
+    logpdf_pi = jax.vmap(logpdf, in_axes=(None, 0, None))(trace.gem, pi_split, K)
+
+    logpdf_clusters = score_trace_cluster(data, trace.g, trace.cluster, max_clusters)
+    logpdf_split_clusters = score_trace_cluster(data, trace.g, q_split_trace[-1], max_clusters, add_c=True)
+
+    return logpdf_pi + logpdf_split_clusters - logpdf_clusters
+
+def score_q_pi(q_pi, max_clusters, alpha):
+    q_pi_dist = Dirichlet(alpha=jnp.ones((1, 2)) * alpha/2)
+    q_pi_stack = Categorical(
+        jnp.vstack((
+        jnp.log(q_pi[:max_clusters]),
+        jnp.log(q_pi[max_clusters:]),
+     ))[None, :])
+
+    return jax.vmap(logpdf, in_axes=(None, -1))(q_pi_dist, q_pi_stack)
+
+def make_pi(pi, k, pi_split, max_clusters):
+    pi_k0 = pi[k]
+    pi = pi.at[k].set(pi_k0 * pi_split[k])
+    idx = jnp.argwhere(pi == 0, size=1)
+    pi = pi.at[idx].set(pi_k0 * pi_split[k + max_clusters])
+
+    return jnp.sort(pi, descending=True)
+
+def score_trace_cluster(data, g, cluster, max_clusters, add_c=False):
+    c, pi, f = cluster.c, cluster.pi, cluster.f
+
+    x_scores = jax.vmap(logpdf, in_axes=(None, 0, 0))(f, data, c)
+
+    c = jnp.mod(c, max_clusters)
+
+    pi_dist = Categorical(logprobs=jnp.log(pi.reshape(1, -1)))
+    theta_scores = jax.vmap(logpdf, in_axes=(None, 0))(g, f)[:max_clusters]
+
+    if add_c:
+        c_scores = jax.vmap(logpdf, in_axes=(None, 0))(pi_dist, c.reshape(-1, 1))
+        x_scores = x_scores + c_scores
+
+    xc_scores_cluster = jax.ops.segment_sum(
+        x_scores, c, 
+        num_segments=max_clusters)
+
+    return xc_scores_cluster + theta_scores
+
+def split_cluster(cluster, split_clusters, k, K, max_clusters):
+    # update pi
+    pi = cluster.pi
+    pi0 = pi[k]
+    pi = pi.at[k].set(pi0 * split_clusters.pi[k])    
+    pi = pi.at[K - 1].set(pi0 * split_clusters.pi[k + max_clusters])    
+
+    # update c
+    c = cluster.c
+    c = jnp.where(c == k, split_clusters.c, c)
+    c = jnp.where(c == k + max_clusters, K-1, c)
+
+    # update f
+    f = update_f(cluster.f, split_clusters.f, k, K-1, max_clusters)
+
+    return Cluster(c, pi, f)
+
+@dispatch
+def update_f(f0: Normal, f: Normal, k: Integer[Array, ""], K: Integer[Array, ""], max_clusters: Integer[Array, ""]):
+    mu = update_vector(f0.mu, f.mu, k, K, max_clusters)
+    std = update_vector(f0.std, f.std, k, K, max_clusters)
+
+    return Normal(mu, std)
+
+@dispatch
+def update_f(f0: Categorical, f: Categorical, k: Integer[Array, ""], K: Integer[Array, ""], max_clusters: Integer[Array, ""]):
+    logprobs = update_vector(f0.logprobs, f.logprobs, k, K, max_clusters)
+
+    return Categorical(logprobs)
+
+@dispatch
+def update_f(f0: Mixed, f: Mixed, k: Integer[Array, ""], K: Integer[Array, ""], max_clusters: Integer[Array, ""]):
+    return Mixed(
+        update_f(f0.normal, f.normal, k, K, max_clusters),
+        update_f(f0.categorical, f.categorical, k, K, max_clusters)
+    )
+
+def update_vector(v0, split_v, k, K, max_clusters):
+    v = v0
+    v = v.at[k].set(split_v[k])
+    v = v.at[K].set(split_v[k + max_clusters])
+    return v
+
+@partial(jax.jit, static_argnames=['gibbs_iters', 'max_clusters'])
+def q_split(data, gibbs_iters, max_clusters, key, c0, alpha, g) -> Cluster:
+    keys = jax.random.split(key, 3)
+    c = (c0 + max_clusters * jax.random.bernoulli(keys[0], shape=c0.shape)).astype(int)
+    # c = jnp.concatenate((jnp.zeros(100), jnp.ones(100) * 2))
+
+    log_likelihood_mask = make_log_likelihood_mask(c0, max_clusters)
+
+    partial_gibbs_step = partial(gibbs_step, 
+        alpha=alpha, g=g, data=data, 
+        log_likelihood_mask=log_likelihood_mask, max_clusters=max_clusters)
+
+    keys = jax.random.split(keys[2], gibbs_iters)
+    _, q_split_trace = jax.lax.scan(partial_gibbs_step, c, keys)
+
+
+    return q_split_trace
+
+def make_log_likelihood_mask(c, max_clusters):
+    log_likelihood_mask = -jnp.inf * jnp.ones((c.shape[0], 2 * np.array(max_clusters, dtype=int)))
+
+    n = jnp.arange(c.shape[0], dtype=int)
+    clusters_x = jnp.concatenate((n, n))
+    clusters_y = jnp.concatenate((c, max_clusters + c), dtype=int)
+    return log_likelihood_mask.at[clusters_x, clusters_y].set(0)
+
+def gibbs_step(assignments, key, alpha, g, data, log_likelihood_mask, max_clusters, rejuvenation=False):
+    subkey1, subkey2, subkey3 = jax.random.split(key, 3)
+    f = gibbs_f(max_clusters, data, subkey1, g, assignments)
+    pi = gibbs_pi(max_clusters, subkey2, alpha, assignments, rejuvenation=rejuvenation)
+
+    assignments = gibbs_c(subkey3, pi, log_likelihood_mask, f, data)
+
+    # now compute the logpdf for the assignments given the new distribution
+    return assignments, Cluster(assignments, pi, f)
+
+def gibbs_f(max_clusters, data, key, g, assignments):
+    g_prime = posterior(g, data, assignments, 2*max_clusters)
+    f = sample(key, g_prime)
+    return f
+
+def gibbs_c(key, pi, log_likelihood_mask, f, data):
+    log_likelihoods = jax.vmap(jax.vmap(logpdf, in_axes=(0, None)), in_axes=(None, 0))(f, data)
+    log_likelihoods = log_likelihoods + log_likelihood_mask
+    log_score = log_likelihoods + jnp.log(pi)
+
+    assignments = jax.random.categorical(key, log_score, axis=-1).astype(int)
+    return assignments
+
+def gibbs_pi(max_clusters, key, alpha, c, rejuvenation=False):
+    cluster_counts = jnp.sum(jax.nn.one_hot(c, num_classes=2 * max_clusters, dtype=jnp.int32), axis=0)
+    if rejuvenation:
+        pi = jax.random.dirichlet(key, cluster_counts)
+        return pi
+    else:
+        pi = jax.random.dirichlet(key, alpha / 2 + cluster_counts)
+        pi_pairs = pi.reshape((2, -1))
+        pi_pairs = pi_pairs / jnp.sum(pi_pairs, axis=0)
+        return pi_pairs.reshape(-1)
\ No newline at end of file
diff --git a/src/genjaxmix/conjugacy.py b/src/genjaxmix/conjugacy.py
index c124f6e..6cae51b 100644
--- a/src/genjaxmix/conjugacy.py
+++ b/src/genjaxmix/conjugacy.py
@@ -2,7 +2,19 @@
 import jax
 from .dpmm import K, L_num
 
-def posterior_normal_inverse_gamma(assignments, x, mu_0=0.0, v_0=1.0, a_0=1.0, b_0=1.0):
+def posterior_normal_inverse_gamma(assignments:jax.Array, x: jax.Array, mu_0:float=0.0, v_0:float=1.0, a_0:float=1.0, b_0:float=1.0):
+    """Compute the posterior parameters of a normal-inverse-gamma distribution given the assignments and data.
+
+    See Section 6 of https://www.cs.ubc.ca/~murphyk/Papers/bayesGauss.pdf by Kevin P. Murphy.
+
+    Args:
+        assignments: an array of integers in [0, K) representing the cluster assignments
+        x: an array of floats representing the data
+        mu_0: the prior mean
+        v_0: the prior precision
+        a_0: the prior shape
+        b_0: the
+    """
     counts = jnp.bincount(assignments, length=K)
     sum_x = jax.ops.segment_sum(x, assignments, K)
     sum_x_sq = jax.ops.segment_sum(x**2, assignments, K)
@@ -13,7 +25,13 @@ def posterior_normal_inverse_gamma(assignments, x, mu_0=0.0, v_0=1.0, a_0=1.0, b
     b = b_0 + 0.5 * (sum_x_sq + 1/v_0*mu_0**2 - v_n_inv * m ** 2)
     return m, 1/v_n_inv, a, b
 
-def posterior_dirichlet(assignments, x):
+def posterior_dirichlet(assignments:jax.Array, x:jax.Array):
+    """Computes the posterior parameters of a Dirichlet distribution for a multinomial likelihood.
+
+    Args:
+        assignments: an array of integers in [0, K) representing the cluster assignments
+        x: an array of integers in [0, L_num) representing the data
+    """
     one_hot_c = jax.nn.one_hot(assignments, K)
     one_hot_y = jax.nn.one_hot(x, L_num)
     frequency_matrix = one_hot_c.T @ one_hot_y
diff --git a/src/genjaxmix/dpmm.py b/src/genjaxmix/dpmm.py
index e59b6c2..726330d 100644
--- a/src/genjaxmix/dpmm.py
+++ b/src/genjaxmix/dpmm.py
@@ -1,7 +1,14 @@
+"""Provides the generative function for a Dirichlet process mixture model.
+
+This module allows the user to create and execute Dirichlet Process Mixture Models (DPMM) using GenJAX.
+"""
+
+
 from genjax import gen, repeat
 from genjax import normal, inverse_gamma, dirichlet, categorical, beta
 import jax.numpy as jnp
 from .utils import beta_to_logpi
+import jax
 
 K = 5 
 L_num = 7
@@ -19,20 +26,20 @@ def hyperparameters(mu_0=0.0, l=1.0, shape=1.0, scale=1.0, alpha=1.0):
     return mu, sigma, logp
 
 @gen
-def cluster(pi, mu, sigma, logp):
+def cluster(pi:jax.Array, mu:jax.Array, sigma:jax.Array, logp:jax.Array):
     """Sample from a mixture model with proportions ``pi``, normal inverse gamma parameters ``mu`` and ``sigma``, and 
     categorical parameter ``logp``.
 
     Args:
-        pi:
-        mu:
-        sigma:
-        logp:
+        pi - a one dimensional array of proportions
+        mu: - a K-dimensional array of means
+        sigma - a K-dimensional array of standard deviations
+        logp - a KxL_num array of log probabilities
     
     Returns:
-        idx:
-        y1:
-        y2:
+        idx - an integer representing the cluster assignment
+        y1 - an array representing the numerical feature
+        y2 - an array representing the categorical feature
 
     """
     idx = categorical(pi) @ "c"
@@ -41,7 +48,7 @@ def cluster(pi, mu, sigma, logp):
     return idx, y1, y2
 
 @gen
-def gem(alpha):
+def gem(alpha:float) -> jnp.ndarray:
     """Sample from a Griffiths, Engen, and McCloskey's (GEM) distribution with concentration ``alpha``.
 
     Args:
@@ -77,24 +84,32 @@ def gem(alpha):
 #     y = cluster_repeat(logpi, mu, sigma, logp) @ "assignments"
 #     return y
 
-def generate(N_max):
-    """Sample from a Dirichlet process mixture model.
+def generate(N_max: int):
+    """ Construct a Dirichlet Procsess Mixture Model with a given number of data points.
 
     Args:
-        concentration: 
-        mu_0: ?
-        precision: ?
-        a: shape of the inverse gamma
-        b: scale of the inverse gamma
+        N_max: maximum number of data points
     
     Returns:
-        A triplet ``(c, y1, y2)`` of three arrays. The first value, ``c``, is the assignments. The values ``y1` and ``y2``
-        represent the numerical and categorical features of each data point, respectively.
+        A generative function that generates a DPMM model with a given number of data points
     """
     cluster_repeat = repeat(n=N_max)(cluster)
 
     @gen
-    def dpmm(concentration=1.0, mu_0=0.0, l=1.0, a=1.0, b=1.0):
+    def dpmm(concentration:float=1.0, mu_0:float=0.0, l:float=1.0, a:float=1.0, b:float=1.0):
+        """Sample from a Dirichlet process mixture model.
+
+        Args:
+            concentration: Dirichlet Process concentration
+            mu_0: mean prior
+            precision: precision prior
+            a: shape of the inverse gamma
+            b: scale of the inverse gamma
+        
+        Returns:
+            A triplet ``(c, y1, y2)`` of three arrays. The first value, ``c``, is the assignments. The values ``y1` and ``y2``
+            represent the numerical and categorical features of each data point, respectively.
+        """
 
         logpi = gem(concentration) @ "pi"
         mu, sigma, logp = hyperparameters(mu_0, l, a, b) @ "hyperparameters"
diff --git a/src/genjaxmix/smc.py b/src/genjaxmix/smc.py
index adeaf19..ab07048 100644
--- a/src/genjaxmix/smc.py
+++ b/src/genjaxmix/smc.py
@@ -8,7 +8,21 @@
 from functools import partial
 
 @partial(jax.jit, static_argnames=['gibbs_iters', 'max_clusters', 'n_steps'])
-def smc(key, trace, data_test, n_steps, data, gibbs_iters, max_clusters):
+def smc(key:jax.random.PRNGKey, trace:Trace, data_test: jax.Array, n_steps:int, data:jax.Array, gibbs_iters:int, max_clusters:int):
+    """Runs a sequential Monte Carlo algorithm for a DPMM.
+
+    Args:
+        key: a PRNG key
+        trace: the initial trace
+        data_test: the test data
+        n_steps: the number of steps
+        data: the data
+        gibbs_iters: the number of Gibbs iterations
+        max_clusters: the maximum number of clusters
+    
+    Returns:
+        A tuple of the final trace and the sum of the log probabilities
+    """
     smc_keys = jax.random.split(key, n_steps)
 
     def wrap_step(trace, n):
@@ -42,7 +56,16 @@ def wrap_step(trace, n):
 
 
 
-def rejuvenate(key, data, trace, gibbs_iters, max_clusters):
+def rejuvenate(key:jax.random.PRNGKey, data:jax.Array, trace:Trace, gibbs_iters:int, max_clusters:int):
+    """Rejuvenate the trace by running rejuvenation moves for the cluster parameters, cluster proportions, and data point assignments.
+
+    Args:
+        key: a PRNG key
+        data: the data
+        trace: the trace
+        gibbs_iters: the number of Gibbs iterations
+        max_clusters: the maximum number of clusters
+    """
     extended_pi = jnp.concatenate((trace.cluster.pi, jnp.zeros(max_clusters)))
     log_likelihood_mask = jnp.where(extended_pi == 0, -jnp.inf, 0)
 
@@ -207,12 +230,30 @@ def gibbs_step(assignments, key, alpha, g, data, log_likelihood_mask, max_cluste
     # now compute the logpdf for the assignments given the new distribution
     return assignments, Cluster(assignments, pi, f)
 
-def gibbs_f(max_clusters, data, key, g, assignments):
+def gibbs_f(max_clusters:int, data:jax.Array, key:jax.random.PRNGKey, g, assignments:jax.Array):
+    """Gibbs move for the cluster parameters.
+
+    Args:
+        max_clusters: the maximum number of clusters
+        data: the data
+        key: a PRNG key
+        g: the prior distribution
+        assignments: the data point assignments
+    """
     g_prime = posterior(g, data, assignments, 2*max_clusters)
     f = sample(key, g_prime)
     return f
 
-def gibbs_c(key, pi, log_likelihood_mask, f, data):
+def gibbs_c(key:jax.random.PRNGKey, pi:jax.Array, log_likelihood_mask:jax.Array, f, data:jax.Array):
+    """Gibbs move for the data point assignments.
+
+    Args:
+        key: a PRNG key
+        pi: the cluster proportions
+        log_likelihood_mask: a mask for the log likelihoods
+        f: the cluster parameters
+        data: the data
+    """
     log_likelihoods = jax.vmap(jax.vmap(logpdf, in_axes=(0, None)), in_axes=(None, 0))(f, data)
     log_likelihoods = log_likelihoods + log_likelihood_mask
     log_score = log_likelihoods + jnp.log(pi)
@@ -220,7 +261,16 @@ def gibbs_c(key, pi, log_likelihood_mask, f, data):
     assignments = jax.random.categorical(key, log_score, axis=-1).astype(int)
     return assignments
 
-def gibbs_pi(max_clusters, key, alpha, c, rejuvenation=False):
+def gibbs_pi(max_clusters:int, key:jax.random.PRNGKey, alpha:float, c:jax.Array, rejuvenation:bool=False):
+    """Gibbs move for the cluster proportions.
+
+    Args:
+        max_clusters: the maximum number of clusters
+        key: a PRNG key
+        alpha: the Dirichlet hyperparameter
+        c: the data point assignments
+        rejuvenation: whether to rejuvenate the cluster proportions
+    """
     cluster_counts = jnp.sum(jax.nn.one_hot(c, num_classes=2 * max_clusters, dtype=jnp.int32), axis=0)
     if rejuvenation:
         pi = jax.random.dirichlet(key, cluster_counts)

From 20a5a20d53a10509c4618ef4de7b1a9c97c0e93d Mon Sep 17 00:00:00 2001
From: Ian Limarta <ian.limarta@gmail.com>
Date: Wed, 8 Jan 2025 23:11:15 -0600
Subject: [PATCH 6/8] ground up DPMM

---
 notebooks/scratch.ipynb | 452 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 452 insertions(+)
 create mode 100644 notebooks/scratch.ipynb

diff --git a/notebooks/scratch.ipynb b/notebooks/scratch.ipynb
new file mode 100644
index 0000000..9dadc03
--- /dev/null
+++ b/notebooks/scratch.ipynb
@@ -0,0 +1,452 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import jax\n",
+    "import jax.numpy as jnp\n",
+    "import matplotlib.pyplot as plt\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "key = jax.random.key(0)\n",
+    "\n",
+    "K_true = 4\n",
+    "D = 3\n",
+    "N = 1000\n",
+    "key, subkey = jax.random.split(key)\n",
+    "mu = jax.random.normal(subkey, (K_true,D))\n",
+    "\n",
+    "key, subkey = jax.random.split(key)\n",
+    "pi_true = jax.random.dirichlet(subkey, jnp.ones(K_true))\n",
+    "y_labels = jax.random.categorical(subkey, pi_true, shape=(N,))\n",
+    "key, subkey = jax.random.split(key)\n",
+    "sigma_hyper = 1.0\n",
+    "data = jax.random.normal(subkey, (N, D)) * sigma_hyper + mu[y_labels]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(data[:, 0], data[:, 1], c=y_labels, cmap='viridis')\n",
+    "plt.scatter(mu[:, 0], mu[:, 1], c='red', marker='x', s=100, label='True means')\n",
+    "plt.legend()\n",
+    "plt.xlabel('Feature 1')\n",
+    "plt.ylabel('Feature 2')\n",
+    "plt.title('Scatter plot of data with colors from y_labels')\n",
+    "plt.colorbar(label='Cluster')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from dataclasses import dataclass\n",
+    "from functools import partial\n",
+    "\n",
+    "@partial(jax.tree_util.register_dataclass,\n",
+    "         data_fields=('alpha', 'assignments', 'mu', 'pi'),\n",
+    "         meta_fields = ())\n",
+    "@dataclass\n",
+    "class Latent:\n",
+    "    alpha: float\n",
+    "    assignments: jax.Array\n",
+    "    mu: jax.Array\n",
+    "    pi: jax.Array\n",
+    "\n",
+    "def gibbs_pi(key, data_NL, latent: Latent):\n",
+    "    counts = jax.numpy.bincount(latent.assignments, length=latent.pi.shape[0])\n",
+    "    pi_new = jax.random.dirichlet(key, counts + latent.alpha)\n",
+    "    return Latent(alpha=latent.alpha, assignments=latent.assignments, mu=latent.mu, pi=pi_new)\n",
+    "\n",
+    "def gibbs_mu(key, data_nL, latent: Latent, i: int):\n",
+    "    N = jnp.count_nonzero(latent.assignments == i)\n",
+    "    sigma_sq_posterior = 1/(1 + N / sigma_hyper**2)\n",
+    "    x_sum = jnp.sum(data_nL[latent.assignments == i], axis=0)\n",
+    "    mu_posterior = sigma_sq_posterior / sigma_hyper**2 * x_sum\n",
+    "    mu_new = jax.random.normal(key, (D,)) * jnp.sqrt(sigma_sq_posterior) + mu_posterior\n",
+    "    return Latent(alpha=latent.alpha, assignments=latent.assignments, mu=latent.mu.at[i].set(mu_new), pi=latent.pi)\n",
+    "\n",
+    "def gibbs_assignments(key, data_nL, latent: Latent, i:int):\n",
+    "    key, subkey = jax.random.split(key)\n",
+    "    pi = latent.pi\n",
+    "    mu = latent.mu\n",
+    "    z_scores = (data_nL[i] - mu) / sigma_hyper\n",
+    "    logp = jax.scipy.stats.norm.logpdf(z_scores)\n",
+    "    logp = jnp.sum(logp, axis=1)\n",
+    "    log_pi = jnp.log(pi)\n",
+    "    logp = logp + log_pi\n",
+    "    key, subkey = jax.random.split(key)\n",
+    "    assignment = jax.random.categorical(subkey, logp)\n",
+    "    new_assignments = latent.assignments.at[i].set(assignment)  \n",
+    "    return Latent(alpha=latent.alpha, assignments=new_assignments, mu=latent.mu, pi=latent.pi)\n",
+    "\n",
+    "def init(key, N, K_max, D):\n",
+    "    key, subkey = jax.random.split(key)\n",
+    "    mu = jax.random.normal(subkey, (K_max,D))\n",
+    "    key, subkey = jax.random.split(key)\n",
+    "    assignments = jax.random.randint(subkey, (N,), 0, K_max)\n",
+    "    pi = jax.random.dirichlet(key, jax.numpy.ones(K_max))\n",
+    "    return Latent(alpha=1.0, assignments=assignments, mu=mu, pi=pi)\n",
+    "\n",
+    "\n",
+    "key = jax.random.key(0)\n",
+    "latent = init(key, N, K_true, D)\n",
+    "\n",
+    "gibbs_pi_jit = jax.jit(gibbs_pi)\n",
+    "gibbs_assignments_jit = gibbs_assignments\n",
+    "gibbs_mu_jit = gibbs_mu\n",
+    "\n",
+    "latent = gibbs_pi_jit(subkey, data, latent)\n",
+    "latent = gibbs_assignments_jit(key, data, latent, 0)\n",
+    "latent = gibbs_mu(subkey, data, latent, 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gibbs_sweep(key, data, latent):\n",
+    "    key, subkey = jax.random.split(key)\n",
+    "    latent = gibbs_pi_jit(subkey, data, latent)\n",
+    "    for i in range(len(data)):\n",
+    "        key, subkey = jax.random.split(key)\n",
+    "        latent = gibbs_assignments_jit(subkey, data, latent, i)\n",
+    "\n",
+    "    for i in range(latent.mu.shape[0]):\n",
+    "        key, subkey = jax.random.split(key)\n",
+    "        latent = gibbs_mu_jit(subkey, data, latent, i)\n",
+    "    return latent\n",
+    "\n",
+    "latent = init(key, N, K_true, D)\n",
+    "latent_history = [latent]\n",
+    "for i in range(20):\n",
+    "    key, subkey = jax.random.split(key)\n",
+    "    latent = gibbs_sweep(key, data, latent)\n",
+    "    latent_history.append(latent)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(data[:, 0], data[:, 1], c=latent.assignments, cmap='viridis', alpha=1.0, label='Data points')\n",
+    "plt.scatter(latent.mu[:, 0], latent.mu[:, 1], c='red', marker='x', s=100, label='Inferred means')\n",
+    "plt.xlabel('Feature 1')\n",
+    "plt.ylabel('Feature 2')\n",
+    "plt.title('Scatter plot of data with inferred latents')\n",
+    "plt.colorbar(label='Cluster')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.animation as animation\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "def animate(i):\n",
+    "    ax.clear()\n",
+    "    latent = latent_history[i]\n",
+    "    ax.scatter(data[:, 0], data[:, 1], c=latent.assignments, cmap='viridis', alpha=0.5, label='Data points')\n",
+    "    ax.scatter(latent.mu[:, 0], latent.mu[:, 1], c='red', marker='x', s=100, label='Inferred means')\n",
+    "    ax.set_xlabel('Feature 1')\n",
+    "    ax.set_ylabel('Feature 2')\n",
+    "    ax.set_title(f'Scatter plot of data with inferred latents (Iteration {i+1})')\n",
+    "    ax.legend()\n",
+    "\n",
+    "ani = animation.FuncAnimation(fig, animate, frames=len(latent_history), interval=200)\n",
+    "\n",
+    "ani.save('latent_evolution.mp4', writer='ffmpeg')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## K-Cluster Vectorized Inference"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Latent(alpha=Array(1., dtype=float32, weak_type=True), assignments=Array([1, 1, 0, 0, 3, 1, 0, 0, 0, 1, 1, 2, 3, 0, 1, 3, 1, 0, 2, 2, 0, 0,\n",
+       "       3, 3, 1, 2, 1, 0, 0, 0, 0, 2, 1, 3, 3, 3, 0, 3, 3, 1, 1, 0, 1, 3,\n",
+       "       1, 0, 1, 2, 3, 1, 0, 1, 1, 1, 0, 3, 2, 2, 2, 1, 2, 1, 1, 1, 3, 3,\n",
+       "       2, 1, 1, 0, 2, 1, 1, 1, 0, 1, 0, 1, 1, 3, 3, 1, 3, 3, 1, 2, 2, 0,\n",
+       "       1, 1, 3, 1, 0, 3, 3, 2, 2, 3, 2, 0, 1, 0, 2, 1, 2, 1, 1, 2, 1, 1,\n",
+       "       3, 2, 1, 3, 0, 3, 0, 1, 1, 1, 3, 0, 2, 1, 1, 0, 0, 1, 0, 2, 1, 1,\n",
+       "       1, 0, 1, 3, 0, 1, 2, 2, 2, 2, 0, 1, 2, 0, 1, 3, 1, 3, 0, 1, 3, 1,\n",
+       "       3, 1, 1, 3, 3, 3, 1, 2, 0, 1, 1, 3, 2, 1, 2, 1, 1, 0, 3, 2, 3, 2,\n",
+       "       1, 2, 2, 0, 1, 0, 1, 0, 3, 1, 1, 3, 3, 1, 0, 3, 1, 3, 2, 1, 1, 1,\n",
+       "       3, 0, 2, 2, 2, 2, 3, 3, 1, 1, 3, 2, 2, 1, 2, 0, 1, 2, 1, 3, 3, 2,\n",
+       "       3, 0, 1, 2, 2, 3, 3, 0, 3, 3, 2, 2, 2, 0, 0, 2, 1, 0, 3, 3, 2, 3,\n",
+       "       1, 3, 2, 1, 3, 1, 3, 2, 2, 2, 3, 3, 1, 1, 0, 1, 3, 1, 3, 0, 0, 2,\n",
+       "       3, 0, 2, 3, 0, 2, 3, 1, 2, 1, 2, 3, 3, 2, 3, 3, 2, 0, 1, 1, 1, 0,\n",
+       "       1, 0, 1, 2, 1, 0, 3, 3, 0, 3, 2, 2, 2, 1, 0, 3, 0, 3, 2, 3, 2, 1,\n",
+       "       1, 2, 3, 2, 0, 0, 1, 2, 0, 3, 0, 3, 0, 0, 0, 0, 1, 0, 0, 1, 1, 3,\n",
+       "       1, 2, 0, 1, 2, 3, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 0, 3, 3, 0, 1,\n",
+       "       0, 3, 3, 1, 1, 2, 0, 1, 3, 0, 3, 2, 3, 2, 3, 0, 0, 1, 2, 0, 3, 0,\n",
+       "       0, 1, 2, 2, 3, 0, 1, 1, 1, 1, 0, 3, 0, 3, 1, 1, 0, 2, 3, 1, 2, 3,\n",
+       "       1, 2, 3, 3, 2, 3, 2, 3, 3, 1, 2, 3, 1, 0, 0, 3, 3, 2, 0, 3, 3, 1,\n",
+       "       3, 3, 1, 3, 2, 3, 1, 3, 0, 1, 3, 2, 2, 1, 0, 1, 1, 3, 2, 3, 3, 1,\n",
+       "       0, 1, 1, 3, 3, 3, 0, 0, 0, 0, 3, 1, 2, 3, 0, 0, 1, 0, 1, 3, 2, 2,\n",
+       "       0, 0, 3, 1, 2, 2, 0, 1, 3, 3, 0, 3, 3, 2, 0, 2, 2, 3, 3, 1, 0, 1,\n",
+       "       0, 2, 3, 2, 0, 0, 2, 2, 1, 3, 1, 2, 0, 0, 1, 3, 1, 2, 3, 0, 0, 0,\n",
+       "       1, 0, 1, 1, 1, 1, 2, 1, 3, 2, 2, 2, 0, 1, 3, 1, 1, 1, 2, 3, 0, 1,\n",
+       "       3, 1, 3, 0, 1, 3, 1, 3, 0, 2, 1, 0, 1, 0, 2, 1, 2, 3, 2, 0, 0, 1,\n",
+       "       1, 0, 2, 0, 0, 2, 1, 2, 3, 3, 2, 0, 3, 0, 3, 2, 2, 1, 0, 3, 0, 2,\n",
+       "       0, 0, 2, 1, 0, 2, 2, 1, 3, 1, 3, 3, 2, 0, 0, 3, 1, 0, 1, 3, 2, 3,\n",
+       "       1, 2, 2, 1, 3, 2, 0, 2, 0, 2, 0, 1, 3, 2, 0, 3, 1, 1, 3, 1, 3, 0,\n",
+       "       1, 3, 0, 2, 1, 3, 1, 2, 1, 2, 3, 0, 3, 0, 0, 2, 0, 0, 2, 2, 1, 0,\n",
+       "       2, 3, 2, 1, 0, 0, 2, 1, 2, 3, 2, 3, 0, 2, 3, 0, 3, 0, 0, 1, 3, 0,\n",
+       "       3, 1, 1, 2, 0, 0, 0, 1, 3, 2, 2, 1, 3, 2, 1, 0, 0, 3, 0, 2, 3, 1,\n",
+       "       1, 3, 1, 1, 0, 2, 3, 1, 3, 0, 0, 2, 2, 1, 2, 2, 0, 2, 2, 3, 3, 3,\n",
+       "       3, 3, 1, 0, 2, 2, 3, 1, 3, 0, 0, 2, 0, 1, 2, 0, 0, 0, 0, 1, 2, 0,\n",
+       "       1, 0, 0, 2, 2, 0, 3, 2, 1, 1, 2, 0, 2, 2, 0, 2, 0, 3, 1, 1, 1, 0,\n",
+       "       3, 0, 2, 0, 3, 2, 0, 2, 2, 3, 0, 1, 1, 2, 1, 0, 3, 1, 2, 0, 0, 3,\n",
+       "       1, 0, 3, 3, 1, 1, 2, 1, 0, 1, 2, 2, 2, 3, 1, 1, 2, 2, 3, 2, 0, 0,\n",
+       "       2, 0, 2, 0, 2, 2, 2, 0, 0, 0, 3, 1, 3, 2, 0, 0, 2, 1, 2, 1, 3, 3,\n",
+       "       1, 1, 1, 3, 3, 2, 0, 1, 1, 3, 2, 2, 2, 2, 1, 1, 0, 1, 2, 3, 3, 0,\n",
+       "       2, 1, 3, 1, 2, 1, 0, 3, 1, 1, 3, 2, 0, 1, 1, 1, 2, 3, 2, 1, 3, 1,\n",
+       "       0, 2, 0, 1, 3, 1, 1, 1, 1, 2, 0, 0, 3, 1, 0, 1, 3, 0, 3, 1, 3, 1,\n",
+       "       0, 1, 3, 1, 3, 3, 0, 1, 1, 1, 3, 3, 3, 0, 2, 2, 3, 3, 3, 1, 0, 1,\n",
+       "       1, 0, 3, 1, 2, 1, 3, 3, 3, 3, 0, 3, 0, 0, 1, 3, 0, 1, 3, 1, 1, 1,\n",
+       "       1, 0, 0, 2, 0, 2, 2, 1, 2, 1, 3, 1, 2, 2, 0, 0, 1, 3, 0, 1, 2, 1,\n",
+       "       3, 0, 2, 2, 1, 1, 1, 1, 3, 0, 3, 3, 0, 3, 2, 1, 1, 1, 1, 1, 2, 3,\n",
+       "       1, 1, 3, 1, 2, 1, 0, 3, 1, 1, 2, 0, 2, 0, 1, 0, 2, 3, 0, 2, 0, 0,\n",
+       "       1, 3, 2, 3, 2, 1, 0, 3, 3, 3], dtype=int32), mu=Array([[ 0.02534657, -0.34630996,  0.70135725],\n",
+       "       [ 0.07830606, -0.38867092,  0.7189667 ],\n",
+       "       [ 0.24621397, -0.4798445 ,  0.9164252 ],\n",
+       "       [ 0.19147222, -0.5728084 ,  0.8341254 ]], dtype=float32), pi=Array([0.22434255, 0.28109145, 0.22385709, 0.2706808 ], dtype=float32))"
+      ]
+     },
+     "execution_count": 143,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def gibbs_pi(key, data_nL, latent: Latent):\n",
+    "    counts = jnp.bincount(latent.assignments, length=latent.pi.shape[0])\n",
+    "    concentrations = jnp.concat([counts, jnp.array([latent.alpha])], axis=0)\n",
+    "    pi_new = jax.random.dirichlet(key, concentrations)[:-1]\n",
+    "    return Latent(alpha=latent.alpha, assignments=latent.assignments, mu=latent.mu, pi=pi_new)\n",
+    "\n",
+    "def gibbs_mu(key, data_nL, latent: Latent):\n",
+    "    K = latent.mu.shape[0]\n",
+    "    counts = jnp.bincount(latent.assignments, length=K)\n",
+    "    sigma_sq_posterior = 1/(1 + counts / sigma_hyper**2)\n",
+    "    x_sum = jax.ops.segment_sum(data_nL, latent.assignments, K)\n",
+    "    mu_posterior = sigma_sq_posterior.reshape(-1,1) * x_sum / sigma_hyper**2 \n",
+    "    mu_new = jax.random.normal(key, (K,D)) * jnp.sqrt(sigma_sq_posterior).reshape(-1,1) + mu_posterior\n",
+    "    return Latent(alpha=latent.alpha, assignments=latent.assignments, mu=mu_new, pi=latent.pi)\n",
+    "\n",
+    "def gibbs_assignments(key, data_nL, latent: Latent):\n",
+    "    log_pi = jnp.log(latent.pi)\n",
+    "    def pdf(x, mu, log_pi):\n",
+    "        z_scores = (x - mu) / sigma_hyper\n",
+    "        log_p = log_pi + jnp.sum(jax.scipy.stats.norm.logpdf(z_scores))\n",
+    "        return log_p\n",
+    "\n",
+    "    log_probs = jax.vmap(jax.vmap(pdf, in_axes=(None, 0, 0)), in_axes=(0, None, None))(data_nL, latent.mu, log_pi)\n",
+    "    assignments_new = jax.random.categorical(key, log_probs)\n",
+    "    return Latent(alpha=latent.alpha, assignments=assignments_new, mu=latent.mu, pi=latent.pi)\n",
+    "\n",
+    "def gibbs_sweep(key, data, latent):\n",
+    "    key, subkey = jax.random.split(key)\n",
+    "    latent = gibbs_pi(subkey, data, latent)\n",
+    "    latent = gibbs_mu(subkey, data, latent)\n",
+    "    latent = gibbs_assignments(subkey, data, latent)\n",
+    "    return latent\n",
+    "\n",
+    "latent = init(key, N, K_true, D)\n",
+    "gibbs_sweep_jit = jax.jit(gibbs_sweep)\n",
+    "gibbs_sweep_jit(key, data, latent)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "key = jax.random.key(0)\n",
+    "latent = init(key, N, K_true, D)\n",
+    "latent_history = []\n",
+    "for i in range(100):\n",
+    "    key, subkey = jax.random.split(key)\n",
+    "    latent = gibbs_sweep_jit(subkey, data, latent)\n",
+    "    if i % 10 == 0:\n",
+    "        latent_history.append(latent)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Array([0.        , 0.3762794 , 0.25034556, 0.3731362 ], dtype=float32)"
+      ]
+     },
+     "execution_count": 148,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "latent.pi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(data[:, 0], data[:, 1], c=latent.assignments, cmap='viridis', alpha=1.0, label='Data points')\n",
+    "plt.scatter(latent.mu[:, 0], latent.mu[:, 1], c='red', marker='x', s=100, label='Inferred means')\n",
+    "plt.xlabel('Feature 1')\n",
+    "plt.ylabel('Feature 2')\n",
+    "plt.title('Scatter plot of data with inferred latents')\n",
+    "plt.colorbar(label='Cluster')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.animation as animation\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "def animate(i):\n",
+    "    ax.clear()\n",
+    "    latent = latent_history[i]\n",
+    "    ax.scatter(data[:, 0], data[:, 1], c=latent.assignments, cmap='viridis', alpha=0.5, label='Data points')\n",
+    "    ax.scatter(latent.mu[:, 0], latent.mu[:, 1], c='red', marker='x', s=100, label='Inferred means')\n",
+    "    ax.set_xlabel('Feature 1')\n",
+    "    ax.set_ylabel('Feature 2')\n",
+    "    ax.set_title(f'Scatter plot of data with inferred latents (Iteration {i+1})')\n",
+    "    ax.legend()\n",
+    "\n",
+    "ani = animation.FuncAnimation(fig, animate, frames=len(latent_history), interval=200)\n",
+    "\n",
+    "ani.save('latent_evolution.mp4', writer='ffmpeg')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 34b1b6e50a4e610574b5a4476be65309bf0583cd Mon Sep 17 00:00:00 2001
From: Ian Limarta <ian.limarta@gmail.com>
Date: Fri, 10 Jan 2025 15:56:40 -0600
Subject: [PATCH 7/8] Notebook explaining split moves

---
 notebooks/split_merge.ipynb | 152 ++++++++++++++++++++++++++++++++++++
 1 file changed, 152 insertions(+)
 create mode 100644 notebooks/split_merge.ipynb

diff --git a/notebooks/split_merge.ipynb b/notebooks/split_merge.ipynb
new file mode 100644
index 0000000..e339ea2
--- /dev/null
+++ b/notebooks/split_merge.ipynb
@@ -0,0 +1,152 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Jain Neal Split-Merge Moves"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Random Split-Merge Procedure"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import jax\n",
+    "import jax.numpy as jnp\n",
+    "import matplotlib.pyplot as plt\n",
+    "from dataclasses import dataclass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "K_max = 10\n",
+    "K_true = 3\n",
+    "D = 2\n",
+    "N = 10\n",
+    "alpha = 1.0\n",
+    "sigma_hyper = 1.0\n",
+    "\n",
+    "key = jax.random.key(0)\n",
+    "\n",
+    "key, *subkeys = jax.random.split(key, 5)\n",
+    "\n",
+    "mu_true = jax.random.normal(subkeys[0], (K_max, D)) * sigma_hyper\n",
+    "\n",
+    "pi_true = jax.random.dirichlet(subkeys[1], jnp.ones(K_true)* alpha)\n",
+    "pi_true = jnp.concat([pi_true, jnp.zeros(K_max-K_true)], axis=0)\n",
+    "\n",
+    "z_true = jax.random.categorical(subkeys[2], jnp.log(pi_true), shape=(N,))\n",
+    "\n",
+    "data = jax.random.normal(subkeys[3], (N,D)) + mu_true[z_true]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 1 0 0 1 1 1 1 0 1]\n",
+      "A: 3.465735912322998 B: -4.094344615936279 C: 0.0\n",
+      "0.53333324\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Array([1, 1, 0, 0, 1, 1, 1, 1, 0, 1], dtype=int32)"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "@dataclass\n",
+    "class Latent:\n",
+    "    K_max: int\n",
+    "    alpha: float\n",
+    "    K: int\n",
+    "    z: jax.Array\n",
+    "\n",
+    "\n",
+    "def partition_log_ratio(alpha, M: int, N: int):\n",
+    "    return jnp.log(alpha) - jax.scipy.special.gammaln(M+N) + jax.scipy.special.gammaln(M) + jax.scipy.special.gammaln(N)\n",
+    "\n",
+    "def proposal_log_ratio(M,N):\n",
+    "    return jnp.log(2)*(M+N-2)\n",
+    "\n",
+    "def likelihood_log_ratio(data, ):\n",
+    "    return 0.0\n",
+    "\n",
+    "def random_split(key, data, latent: Latent, i: int, j: int):\n",
+    "    c_next = jnp.max(latent.z)+1\n",
+    "    key, subkey = jax.random.split(key)\n",
+    "    splits = jnp.where(jax.random.bernoulli(subkey, shape=(latent.z.shape[0],)), latent.z[i], c_next)\n",
+    "    mask = latent.z == latent.z[i]\n",
+    "    z_proposal = jnp.where(mask, splits, latent.z)\n",
+    "    z_proposal = z_proposal.at[i].set(latent.z[i])\n",
+    "    z_proposal = z_proposal.at[j].set(c_next)\n",
+    "    \n",
+    "    Ni = jnp.count_nonzero(z_proposal == latent.z[i])\n",
+    "    Nj = jnp.count_nonzero(z_proposal == c_next)\n",
+    "\n",
+    "    A = proposal_log_ratio(Ni,Nj)\n",
+    "    B = partition_log_ratio(alpha, Ni, Nj)\n",
+    "    C = likelihood_log_ratio(1,1)\n",
+    "    print(f\"A: {A} B: {B} C: {C}\")\n",
+    "\n",
+    "    a = jnp.exp(min(0, A + B + C))\n",
+    "    print(a)\n",
+    "    key, subkey = jax.random.split(key)\n",
+    "    u = jax.random.uniform(subkey)\n",
+    "    z_new = jnp.where(u < a, z_proposal, latent.z)\n",
+    "    return z_new\n",
+    "\n",
+    "\n",
+    "key = jax.random.key(2)\n",
+    "latent = Latent(K_max, alpha, 2, jax.random.categorical(key, jnp.ones(2), shape=(N,)))\n",
+    "print(latent.z)\n",
+    "random_split(key, data, latent, 0, 1)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 8d7130f144d12e9a71db35f6bcdf4c19d48ae83b Mon Sep 17 00:00:00 2001
From: Ian Limarta <ian.limarta@gmail.com>
Date: Thu, 16 Jan 2025 14:49:17 -0500
Subject: [PATCH 8/8] Tutorial docs

---
 docs/tutorials/dpmm_ground_up.ipynb           | 266 ++++++++
 docs/tutorials/getting_started.md             |  16 -
 .../tutorials/{test.py => getting_started.py} |   0
 docs/tutorials/heart_disease.md               |   1 -
 .../tutorials/inferred_clusters_animation.mp4 | Bin 0 -> 30430 bytes
 docs/tutorials/sub_cluster.ipynb              | 606 ++++++++++++++++++
 mkdocs.yml                                    |   4 +-
 notebooks/scratch.ipynb                       | 452 -------------
 8 files changed, 875 insertions(+), 470 deletions(-)
 create mode 100644 docs/tutorials/dpmm_ground_up.ipynb
 delete mode 100644 docs/tutorials/getting_started.md
 rename docs/tutorials/{test.py => getting_started.py} (100%)
 delete mode 100644 docs/tutorials/heart_disease.md
 create mode 100644 docs/tutorials/inferred_clusters_animation.mp4
 create mode 100644 docs/tutorials/sub_cluster.ipynb
 delete mode 100644 notebooks/scratch.ipynb

diff --git a/docs/tutorials/dpmm_ground_up.ipynb b/docs/tutorials/dpmm_ground_up.ipynb
new file mode 100644
index 0000000..62960e4
--- /dev/null
+++ b/docs/tutorials/dpmm_ground_up.ipynb
@@ -0,0 +1,266 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import jax\n",
+    "import jax.numpy as jnp\n",
+    "import matplotlib.pyplot as plt\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "key = jax.random.key(0)\n",
+    "\n",
+    "K_true = 4\n",
+    "D = 2\n",
+    "N = 1000\n",
+    "key, subkey = jax.random.split(key)\n",
+    "mu = jax.random.normal(subkey, (K_true,D))\n",
+    "\n",
+    "key, subkey = jax.random.split(key)\n",
+    "pi_true = jax.random.dirichlet(subkey, jnp.ones(K_true))\n",
+    "y_labels = jax.random.categorical(subkey, pi_true, shape=(N,))\n",
+    "key, subkey = jax.random.split(key)\n",
+    "sigma_hyper = 0.5\n",
+    "data = jax.random.normal(subkey, (N, D)) * sigma_hyper + mu[y_labels]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from dataclasses import dataclass\n",
+    "from functools import partial\n",
+    "\n",
+    "@partial(jax.tree_util.register_dataclass,\n",
+    "         data_fields=('alpha', 'assignments', 'mu', 'pi'),\n",
+    "         meta_fields = ())\n",
+    "@dataclass\n",
+    "class Latent:\n",
+    "    alpha: float\n",
+    "    assignments: jax.Array\n",
+    "    mu: jax.Array\n",
+    "    pi: jax.Array\n",
+    "\n",
+    "def gibbs_pi(key, data_NL, latent: Latent):\n",
+    "    counts = jax.numpy.bincount(latent.assignments, length=latent.pi.shape[0])\n",
+    "    pi_new = jax.random.dirichlet(key, counts + latent.alpha)\n",
+    "    return Latent(alpha=latent.alpha, assignments=latent.assignments, mu=latent.mu, pi=pi_new)\n",
+    "\n",
+    "def gibbs_mu(key, data_nL, latent: Latent, i: int):\n",
+    "    N = jnp.count_nonzero(latent.assignments == i)\n",
+    "    sigma_sq_posterior = 1/(1 + N / sigma_hyper**2)\n",
+    "    x_sum = jnp.sum(data_nL[latent.assignments == i], axis=0)\n",
+    "    mu_posterior = sigma_sq_posterior / sigma_hyper**2 * x_sum\n",
+    "    mu_new = jax.random.normal(key, (D,)) * jnp.sqrt(sigma_sq_posterior) + mu_posterior\n",
+    "    return Latent(alpha=latent.alpha, assignments=latent.assignments, mu=latent.mu.at[i].set(mu_new), pi=latent.pi)\n",
+    "\n",
+    "def gibbs_assignments(key, data_nL, latent: Latent, i:int):\n",
+    "    key, subkey = jax.random.split(key)\n",
+    "    pi = latent.pi\n",
+    "    mu = latent.mu\n",
+    "    z_scores = (data_nL[i] - mu) / sigma_hyper\n",
+    "    logp = jax.scipy.stats.norm.logpdf(z_scores)\n",
+    "    logp = jnp.sum(logp, axis=1)\n",
+    "    log_pi = jnp.log(pi)\n",
+    "    logp = logp + log_pi\n",
+    "    key, subkey = jax.random.split(key)\n",
+    "    assignment = jax.random.categorical(subkey, logp)\n",
+    "    new_assignments = latent.assignments.at[i].set(assignment)  \n",
+    "    return Latent(alpha=latent.alpha, assignments=new_assignments, mu=latent.mu, pi=latent.pi)\n",
+    "\n",
+    "def init(key, N, K_max, D):\n",
+    "    key, subkey = jax.random.split(key)\n",
+    "    mu = jax.random.normal(subkey, (K_max,D))\n",
+    "    key, subkey = jax.random.split(key)\n",
+    "    assignments = jax.random.randint(subkey, (N,), 0, K_max)\n",
+    "    pi = jax.random.dirichlet(key, jax.numpy.ones(K_max))\n",
+    "    return Latent(alpha=1.0, assignments=assignments, mu=mu, pi=pi)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Inference"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Latent(alpha=Array(1., dtype=float32, weak_type=True), assignments=Array([2, 2, 2, 1, 2, 0, 2, 3, 1, 1, 2, 1, 3, 0, 0, 0, 0, 1, 2, 2, 0, 2,\n",
+       "       3, 2, 1, 3, 0, 1, 2, 3, 1, 2, 3, 3, 2, 0, 0, 3, 2, 0, 3, 3, 2, 1,\n",
+       "       0, 1, 0, 1, 1, 2, 0, 3, 3, 2, 3, 0, 2, 2, 2, 2, 1, 2, 1, 2, 1, 2,\n",
+       "       2, 2, 3, 2, 2, 2, 3, 3, 2, 2, 3, 2, 0, 3, 0, 3, 0, 2, 0, 0, 0, 1,\n",
+       "       1, 2, 2, 1, 1, 3, 2, 3, 1, 3, 2, 3, 3, 2, 1, 0, 2, 3, 2, 1, 0, 3,\n",
+       "       3, 3, 2, 0, 2, 0, 3, 3, 3, 0, 3, 0, 1, 3, 0, 0, 0, 1, 3, 0, 1, 2,\n",
+       "       3, 1, 2, 3, 1, 0, 0, 1, 0, 1, 2, 2, 2, 2, 1, 1, 3, 2, 3, 1, 0, 0,\n",
+       "       3, 3, 1, 2, 0, 1, 1, 1, 2, 0, 1, 1, 2, 2, 0, 0, 0, 0, 1, 0, 3, 2,\n",
+       "       0, 0, 1, 0, 0, 0, 3, 3, 2, 2, 1, 3, 1, 2, 1, 0, 2, 2, 2, 1, 0, 2,\n",
+       "       2, 2, 2, 2, 0, 3, 0, 0, 1, 2, 2, 0, 2, 0, 0, 0, 0, 2, 2, 2, 0, 2,\n",
+       "       3, 2, 2, 2, 2, 2, 1, 0, 1, 2, 3, 1, 2, 2, 0, 1, 2, 0, 1, 3, 1, 0,\n",
+       "       1, 0, 2, 3, 2, 2, 2, 0, 1, 2, 3, 0, 2, 2, 2, 3, 3, 2, 3, 3, 1, 1,\n",
+       "       1, 0, 1, 1, 0, 3, 2, 2, 2, 3, 2, 1, 1, 3, 3, 0, 3, 0, 3, 1, 0, 3,\n",
+       "       2, 1, 3, 3, 1, 3, 1, 1, 0, 0, 3, 0, 1, 2, 0, 1, 1, 3, 0, 2, 1, 2,\n",
+       "       3, 2, 3, 0, 3, 2, 2, 2, 2, 1, 3, 0, 0, 1, 0, 2, 2, 2, 2, 2, 3, 3,\n",
+       "       0, 2, 0, 0, 1, 2, 0, 2, 2, 1, 3, 1, 2, 2, 1, 0, 2, 1, 2, 3, 3, 3,\n",
+       "       1, 1, 1, 1, 1, 0, 2, 0, 0, 2, 1, 0, 2, 0, 1, 0, 3, 0, 3, 1, 0, 1,\n",
+       "       0, 1, 1, 2, 2, 1, 3, 0, 0, 2, 2, 1, 2, 1, 3, 0, 2, 1, 2, 0, 0, 0,\n",
+       "       0, 0, 2, 3, 3, 2, 0, 1, 0, 2, 3, 1, 2, 2, 2, 0, 1, 1, 3, 2, 0, 2,\n",
+       "       3, 3, 2, 1, 0, 1, 2, 3, 2, 0, 1, 0, 1, 0, 3, 3, 1, 2, 0, 3, 3, 1,\n",
+       "       0, 2, 0, 2, 0, 3, 1, 2, 2, 0, 3, 1, 3, 1, 0, 1, 2, 1, 2, 3, 0, 0,\n",
+       "       1, 3, 1, 0, 2, 0, 3, 0, 0, 1, 2, 3, 2, 2, 0, 2, 2, 3, 1, 1, 1, 2,\n",
+       "       1, 0, 1, 3, 1, 1, 3, 1, 0, 3, 0, 2, 0, 1, 3, 0, 0, 2, 2, 3, 3, 0,\n",
+       "       2, 3, 0, 1, 1, 3, 3, 1, 3, 1, 3, 3, 2, 2, 2, 0, 2, 1, 0, 3, 2, 2,\n",
+       "       3, 2, 2, 3, 1, 2, 3, 2, 3, 0, 1, 3, 1, 2, 0, 0, 3, 3, 3, 1, 1, 3,\n",
+       "       0, 2, 0, 2, 2, 0, 1, 2, 2, 2, 2, 0, 2, 1, 3, 0, 3, 2, 2, 1, 3, 0,\n",
+       "       3, 3, 3, 2, 1, 0, 2, 2, 2, 3, 3, 0, 0, 2, 2, 1, 0, 1, 1, 1, 0, 0,\n",
+       "       2, 3, 2, 3, 1, 2, 2, 3, 3, 0, 2, 0, 2, 2, 0, 0, 2, 2, 2, 2, 3, 0,\n",
+       "       1, 2, 2, 1, 1, 2, 1, 0, 3, 2, 0, 0, 2, 1, 3, 0, 1, 3, 0, 0, 0, 3,\n",
+       "       1, 2, 1, 2, 2, 1, 0, 3, 1, 2, 1, 1, 2, 2, 2, 2, 0, 0, 3, 3, 1, 0,\n",
+       "       2, 3, 2, 0, 1, 1, 2, 2, 1, 3, 2, 1, 1, 1, 3, 2, 3, 1, 3, 2, 1, 2,\n",
+       "       1, 3, 1, 1, 1, 0, 1, 2, 3, 0, 2, 0, 1, 0, 0, 0, 3, 2, 0, 0, 3, 0,\n",
+       "       1, 1, 2, 1, 2, 2, 2, 1, 0, 2, 2, 1, 3, 3, 2, 0, 2, 3, 3, 0, 0, 1,\n",
+       "       2, 1, 3, 3, 1, 3, 1, 2, 3, 0, 3, 2, 0, 1, 2, 1, 3, 1, 1, 2, 2, 2,\n",
+       "       3, 0, 2, 2, 3, 1, 3, 0, 2, 2, 3, 3, 2, 3, 2, 0, 2, 2, 3, 2, 1, 2,\n",
+       "       3, 2, 2, 0, 2, 2, 2, 2, 1, 0, 0, 0, 0, 3, 2, 1, 3, 0, 1, 1, 1, 2,\n",
+       "       3, 0, 0, 0, 2, 3, 3, 0, 0, 2, 0, 1, 3, 2, 2, 0, 2, 2, 2, 2, 3, 3,\n",
+       "       3, 2, 3, 1, 2, 3, 0, 2, 3, 0, 3, 2, 1, 0, 0, 3, 1, 1, 0, 2, 0, 2,\n",
+       "       2, 2, 0, 0, 3, 2, 2, 0, 2, 0, 0, 3, 2, 0, 1, 0, 2, 3, 3, 0, 1, 3,\n",
+       "       3, 0, 0, 3, 3, 1, 2, 2, 1, 1, 0, 3, 1, 2, 3, 1, 0, 3, 0, 3, 1, 2,\n",
+       "       0, 3, 1, 1, 2, 1, 3, 2, 2, 1, 2, 2, 2, 0, 1, 0, 3, 3, 3, 1, 2, 0,\n",
+       "       0, 3, 2, 0, 3, 0, 0, 3, 1, 2, 3, 0, 2, 0, 2, 3, 2, 3, 0, 0, 1, 3,\n",
+       "       3, 3, 1, 3, 1, 3, 1, 0, 3, 2, 2, 0, 1, 2, 1, 1, 2, 2, 1, 1, 2, 0,\n",
+       "       1, 1, 2, 2, 0, 0, 0, 3, 1, 0, 1, 0, 3, 2, 2, 1, 3, 2, 0, 3, 3, 2,\n",
+       "       3, 2, 3, 1, 3, 2, 3, 2, 2, 3, 1, 0, 3, 1, 3, 3, 1, 0, 3, 1, 0, 0,\n",
+       "       0, 0, 0, 1, 1, 1, 0, 3, 3, 0], dtype=int32), mu=Array([[-0.10152432,  0.4275491 ],\n",
+       "       [-0.189958  ,  0.30651692],\n",
+       "       [-0.09727768,  0.32917628],\n",
+       "       [-0.05243107,  0.35936815]], dtype=float32), pi=Array([0.2523054 , 0.23154502, 0.28939706, 0.22556628], dtype=float32))"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def gibbs_pi(key, data_nL, latent: Latent):\n",
+    "    counts = jnp.bincount(latent.assignments, length=latent.pi.shape[0])\n",
+    "    concentrations = jnp.concat([counts, jnp.array([latent.alpha])], axis=0)\n",
+    "    pi_new = jax.random.dirichlet(key, concentrations)[:-1]\n",
+    "    return Latent(alpha=latent.alpha, assignments=latent.assignments, mu=latent.mu, pi=pi_new)\n",
+    "\n",
+    "def gibbs_mu(key, data_nL, latent: Latent):\n",
+    "    K = latent.mu.shape[0]\n",
+    "    counts = jnp.bincount(latent.assignments, length=K)\n",
+    "    sigma_sq_posterior = 1/(1 + counts / sigma_hyper**2)\n",
+    "    x_sum = jax.ops.segment_sum(data_nL, latent.assignments, K)\n",
+    "    mu_posterior = sigma_sq_posterior.reshape(-1,1) * x_sum / sigma_hyper**2 \n",
+    "    mu_new = jax.random.normal(key, (K,D)) * jnp.sqrt(sigma_sq_posterior).reshape(-1,1) + mu_posterior\n",
+    "    return Latent(alpha=latent.alpha, assignments=latent.assignments, mu=mu_new, pi=latent.pi)\n",
+    "\n",
+    "def gibbs_assignments(key, data_nL, latent: Latent):\n",
+    "    log_pi = jnp.log(latent.pi)\n",
+    "    def pdf(x, mu, log_pi):\n",
+    "        z_scores = (x - mu) / sigma_hyper\n",
+    "        log_p = log_pi + jnp.sum(jax.scipy.stats.norm.logpdf(z_scores))\n",
+    "        return log_p\n",
+    "\n",
+    "    log_probs = jax.vmap(jax.vmap(pdf, in_axes=(None, 0, 0)), in_axes=(0, None, None))(data_nL, latent.mu, log_pi)\n",
+    "    assignments_new = jax.random.categorical(key, log_probs)\n",
+    "    return Latent(alpha=latent.alpha, assignments=assignments_new, mu=latent.mu, pi=latent.pi)\n",
+    "\n",
+    "def gibbs_sweep(key, data, latent):\n",
+    "    key, subkey = jax.random.split(key)\n",
+    "    latent = gibbs_pi(subkey, data, latent)\n",
+    "    latent = gibbs_mu(subkey, data, latent)\n",
+    "    latent = gibbs_assignments(subkey, data, latent)\n",
+    "    return latent\n",
+    "\n",
+    "latent = init(key, N, K_true, D)\n",
+    "gibbs_sweep_jit = jax.jit(gibbs_sweep)\n",
+    "gibbs_sweep_jit(key, data, latent)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "key = jax.random.key(0)\n",
+    "latent = init(key, N, K_true, D)\n",
+    "latent_history = []\n",
+    "for i in range(100):\n",
+    "    key, subkey = jax.random.split(key)\n",
+    "    latent = gibbs_sweep_jit(subkey, data, latent)\n",
+    "    if i % 10 == 0:\n",
+    "        latent_history.append(latent)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjEAAAHHCAYAAABOTAltAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOyddZwU5RvAvzO7t9cJR8PRjXSnSIh0C0r9lFARG0GRFBFQwAAUEVBEukO6pbu767juu72deX9/7N3Ccrt7e0HJfD+fFW/mnfd9ZjbmmSclIYRAQ0NDQ0NDQ+M5Q37aAmhoaGhoaGhoZAZNidHQ0NDQ0NB4LtGUGA0NDQ0NDY3nEk2J0dDQ0NDQ0Hgu0ZQYDQ0NDQ0NjecSTYnR0NDQ0NDQeC7RlBgNDQ0NDQ2N5xJNidHQ0NDQ0NB4LtGUGA0NDQ0NDY3nEk2J0XimadSoEY0aNXraYlgRHBxMp06dyJEjB5IkMWXKlAwdf+3aNSRJYs6cOY9FvidJ4cKF6d27t9NjW7Vqlem1JEli5MiRmTrWZDIxePBgChYsiCzLtGvXLtNyPClGjhyJJEnpjuvduzeFCxd+/AJpaDyDaErMU+DkyZN06tSJoKAg3NzcyJ8/P02bNuWnn356bGv+/fffNm+2d+7cYeTIkRw7duyxrf00iI+PZ+TIkWzfvj3b5/7oo4/YsGEDQ4cOZe7cubz66qvZvoY99uzZw8iRI4mMjHxia2aEM2fOMHLkSK5du/a0RbFi1qxZTJw4kU6dOvHHH3/w0UcfPW2Rngm++eYbVqxY8UTWsvcbpKGRFfRPW4AXjT179vDyyy9TqFAh+vbtS548ebh58yb79u3jhx9+4P33338s6/7999+cOnWKDz/80Gr7nTt3GDVqFIULF6ZSpUqPZe2nQXx8PKNGjQLIdkvO1q1badu2LZ9++mm2zusMe/bsYdSoUfTu3Rs/P78nvv6jnD9/Hll+8Cx05swZRo0aRaNGjbLdOpCQkIBen7mfrK1bt5I/f34mT56crTI973zzzTd06tTpiVim7P0GaWhkBU2JecKMHTsWX19fDh48mOYmdP/+/acj1GMgLi4OT0/Ppy3GY+H+/fvPhALxLODq6vrE1nJzc8v0sdn9ngkhSExMxN3dPc2+xMREDAaDlXKnoaHxmBAaT5RSpUqJRo0aOT1+7ty5onr16sLd3V34+fmJ+vXriw0bNlj2r1ixQrz22msib968wmAwiKJFi4rRo0cLk8lkGdOwYUMBWL2CgoLEtm3b0mwHxOzZsy3H7tu3TzRv3lz4+PgId3d30aBBA7F7924rGUeMGCEAcfr0adGtWzfh5+cnKlWqZPecZs+eLQCxY8cO0a9fPxEQECC8vb1Fjx49RHh4uNXYhg0bioYNG1ptCw4OFv/73/9Erly5hKurq3jppZfEnDlzLPuvXr1q87xGjBjh8FpfvnxZdOrUSfj7+wt3d3dRs2ZNsWbNmjRyP/pyREREhOjVq5fw8fERvr6+omfPnuLo0aNprvPx48dFr169RJEiRYSrq6vInTu36NOnjwgNDbWMSb3Oj76uXr0qhBBi1qxZ4uWXXxaBgYHCYDCIMmXKiGnTpjmUTwghVq5cKQBx/Phxy7YlS5YIQLRv395qbOnSpUWXLl0sfwcFBYlevXo5vD7btm2zjG3ZsqXYtWuXqF69unB1dRVFihQRf/zxR7oyCiHSvIep1+PixYuiV69ewtfXV/j4+IjevXuLuLg4IYT9z0KqTIqiiMmTJ4uyZcsKV1dXkStXLtGvX780n8NU2devXy+qVq0qXF1dxeTJky3fofnz54svv/xS5MuXT0iSJCIiIoQQzn1/hBBi165dolq1asLV1VUULVpU/PLLL5bzS49evXqJoKAgq20TJ04UtWvXFgEBAcLNzU1UqVJFLF68OM31fPSV+l4KIcStW7dEnz59RK5cuYTBYBBly5YVv//+u9Ucqee/cOFC8fXXX4v8+fMLV1dX0bhxY3Hx4kXLOHu/Qan8+OOPomzZspbfuapVq4p58+ale+4aGpol5gkTFBTE3r17OXXqFOXLl3c4dtSoUYwcOZI6deowevRoDAYD+/fvZ+vWrTRr1gyAOXPm4OXlxccff4yXlxdbt25l+PDhREdHM3HiRAC+/PJLoqKiuHXrlsWc7uXlRZkyZRg9ejTDhw+nX79+1K9fH4A6deoAZhN8ixYtqFq1KiNGjECWZWbPnk3jxo3ZtWsXNWrUsJK3c+fOlChRgm+++QYhRLrXYuDAgfj5+TFy5EjOnz/P9OnTuX79Otu3b7cb0JiQkECjRo24dOkSAwcOpEiRIixevJjevXsTGRnJBx98QGBgINOnT+edd96hffv2dOjQAYCXXnrJrizBwcHUqVOH+Ph4Bg0aRI4cOfjjjz9o06YNS5YsoX379jRo0IC5c+fSo0cPmjZtSs+ePR2enxCCtm3bsnv3bgYMGECZMmVYvnw5vXr1SjN206ZNXLlyhT59+pAnTx5Onz7NjBkzOH36NPv27UOSJDp06MCFCxeYP38+kydPJmfOnAAEBgYCMH36dMqVK0ebNm3Q6/WsXr2ad999F1VVee+99+zKWa9ePSRJYufOnZZrtGvXLmRZZvfu3ZZxISEhnDt3joEDB9qcp0GDBgwaNIgff/yRL774gjJlygBY/gW4dOkSnTp14q233qJXr17MmjWL3r17U7VqVcqVK+fwetqjS5cuFClShHHjxnHkyBFmzpxJrly5GD9+PIGBgcydO5exY8cSGxvLuHHjrGTq378/c+bMoU+fPgwaNIirV6/y888/c/ToUf79919cXFws65w/f55u3brRv39/+vbtS6lSpSz7xowZg8Fg4NNPPyUpKQmDweD09+fkyZM0a9aMwMBARo4ciclkYsSIEeTOnTtT1wPghx9+oE2bNrzxxhsYjUYWLFhA586dWbNmDS1btgRg7ty5vP3229SoUYN+/foBUKxYMcD8fahVqxaSJDFw4EACAwP5559/eOutt4iOjk7jEvr222+RZZlPP/2UqKgoJkyYwBtvvMH+/fsB+79BAL/99huDBg2iU6dOfPDBByQmJnLixAn2799P9+7dM30NNF4QnrYW9aKxceNGodPphE6nE7Vr1xaDBw8WGzZsEEaj0WrcxYsXhSzLon379kJRFKt9qqpa/j8+Pj7NGv379xceHh4iMTHRsq1ly5ZpntaEEOLgwYNprAKpa5QoUUI0b948zXpFihQRTZs2tWxLfWLs1q2bU9cg9Ym9atWqVuc9YcIEAYiVK1datj1qiZkyZYoAxF9//WXZZjQaRe3atYWXl5eIjo4WQggREhLilPUllQ8//FAAYteuXZZtMTExokiRIqJw4cJW7wEg3nvvvXTnXLFihQDEhAkTLNtMJpOoX79+mmtu632cP3++AMTOnTst2yZOnGhlfXkYW3M0b95cFC1aNF1Zy5UrZ2VhqVKliujcubMAxNmzZ4UQQixbtiyNxeZhS4wQQixevNjK0vEwQUFBac7n/v37wtXVVXzyySfpyvjo+5n6ufvf//5nNa59+/YiR44cVtsaNmwoypUrZ7Vt165dAkjzxL9+/fo021NlX79+vdXYVEtE0aJFra5/Rr4/7dq1E25ubuL69euWbWfOnBE6nS7TlphHPwtGo1GUL19eNG7c2Gq7p6en1fuXyltvvSXy5s1rZQkUQojXX39d+Pr6WuZPPf8yZcqIpKQky7gffvhBAOLkyZOWbfZ+g9q2bZvmvdHQcBbNafuEadq0KXv37qVNmzYcP36cCRMm0Lx5c/Lnz8+qVass41asWIGqqgwfPjyNb/1hK8XDPvmYmBhCQ0OpX78+8fHxnDt3LtNyHjt2jIsXL9K9e3fCwsIIDQ0lNDSUuLg4XnnlFXbu3ImqqlbHDBgwIENr9OvXz+pJ95133kGv17Nu3Tq7x6xbt448efLQrVs3yzYXFxcGDRpEbGwsO3bsyJAMD89bo0YN6tWrZ9nm5eVFv379uHbtGmfOnMnUnHq9nnfeeceyTafT2Qzefvh9TExMJDQ0lFq1agFw5MgRp9Z7eI6oqChCQ0Np2LAhV65cISoqyuGx9evXZ9euXYD5c3T8+HH69etHzpw5Ldt37dqFn59fuhZER5QtW9Zi8QOzFalUqVJcuXIl03M++rmrX78+YWFhREdHOzxu8eLF+Pr60rRpU8vnOzQ0lKpVq+Ll5cW2bdusxhcpUoTmzZvbnKtXr15W19/Z74+iKGzYsIF27dpRqFAhy/FlypSxu5YzPCxLREQEUVFR1K9f36nPkhCCpUuX0rp1a4QQVtemefPmREVFpZmnT58+GAwGy9+p77Ez76ufnx+3bt3i4MGDzp6ehoYFzZ30FKhevTrLli3DaDRy/Phxli9fzuTJk+nUqRPHjh2jbNmyXL58GVmWKVu2rMO5Tp8+zbBhw9i6dWuaH+30blyOuHjxIoBN18fD8/v7+1v+LlKkSIbWKFGihNXfXl5e5M2b12F67vXr1ylRokQaxS7VPXD9+vUMyfDwvDVr1kyz/eF5M3rzvn79Onnz5rWYzVN52A2RSnh4OKNGjWLBggVpArydfR///fdfRowYwd69e4mPj08zh6+vr91j69evzy+//MKlS5e4fPkykiRRu3Zti3LTt29fdu3aRd26dbMUsPrwjToVf39/IiIism3O1M9kREQEPj4+do+7ePEiUVFR5MqVy+b+R98HR5/vR/c5+/1JSkoiISEhzXcBzJ8TRwq9I9asWcPXX3/NsWPHSEpKsmx3pu5MSEgIkZGRzJgxgxkzZtgc8+i1cfQepMfnn3/O5s2bqVGjBsWLF6dZs2Z0796dunXrpnushoamxDxFDAYD1atXp3r16pQsWZI+ffqwePFiRowY4dTxkZGRNGzYEB8fH0aPHk2xYsVwc3PjyJEjfP7552ksJRkh9diJEyfaTb1+9OZsK1NDwzm6dOnCnj17+Oyzz6hUqRJeXl6oqsqrr77q1Pt4+fJlXnnlFUqXLs2kSZMoWLAgBoOBdevWMXny5HTnSLVA7dy5kytXrlClShU8PT2pX78+P/74I7GxsRw9epSxY8dm6Tx1Op3N7cKJGKrsnlNVVXLlysW8efNs7k+NNUrF0ef70X3Ofn8eVjCyi127dtGmTRsaNGjAtGnTyJs3Ly4uLsyePZu///473eNTZX/zzTftKmGPxpdl5X0tU6YM58+fZ82aNaxfv56lS5cybdo0hg8fbimToKFhD02JeUaoVq0aAHfv3gXMAXaqqnLmzBm7P4Lbt28nLCyMZcuW0aBBA8v2q1evphlr7wnM3vbUAD8fHx+aNGni9HlkhIsXL/Lyyy9b/o6NjeXu3bu89tprdo8JCgrixIkTqKpqZRFIdZ0FBQUBzj1xPjrv+fPn02x/dN6MzrllyxZiY2OtFL5H14mIiGDLli2MGjWK4cOHW7anPs0/jL3zWr16NUlJSaxatcrqqfhRl4g9ChUqRKFChdi1axdXrlyxuAMaNGjAxx9/zOLFi1EUxepzZouMXvenSbFixdi8eTN169bNdgXc2e9PYGAg7u7uNt9rW59HZ1i6dClubm5s2LDBKgV+9uzZacbaer8CAwPx9vZGUZRs/e47+mx4enrStWtXunbtitFopEOHDowdO5ahQ4dmKbVe47+PFhPzhNm2bZvNp5NUs3Gqq6Fdu3bIsszo0aPTPEWnHp/69PPwfEajkWnTpqWZ39PT06ZbIrWWy6MVYKtWrUqxYsX47rvviI2NTXNcSEiI3XN0lhkzZpCcnGz5e/r06ZhMJlq0aGH3mNdee4179+6xcOFCyzaTycRPP/2El5cXDRs2BMDDwwNIe16O5j1w4AB79+61bIuLi2PGjBkULlw4XbeevTlNJhPTp0+3bFMUJU1lZlvvI2Czuqm998vWHFFRUTZvXPaoX78+W7du5cCBAxYlplKlSnh7e/Ptt9/i7u5O1apVHc5hT75nkS5duqAoCmPGjEmzz2QyZekcnP3+6HQ6mjdvzooVK7hx44Zl/9mzZ9mwYUOm1tbpdEiShKIolm3Xrl2zWZnX09PT5mepY8eOLF26lFOnTtmVPaPY+w0KCwuz+ttgMFC2bFmEEFa/DxoattAsMU+Y999/n/j4eNq3b0/p0qUxGo3s2bOHhQsXUrhwYfr06QNA8eLF+fLLLxkzZgz169enQ4cOuLq6cvDgQfLly8e4ceOoU6cO/v7+9OrVi0GDBiFJEnPnzrWpJFWtWpWFCxfy8ccfU716dby8vGjdujXFihXDz8+PX375BW9vbzw9PalZsyZFihRh5syZtGjRgnLlytGnTx/y58/P7du32bZtGz4+PqxevTpL18JoNPLKK6/QpUsXzp8/z7Rp06hXrx5t2rSxe0y/fv349ddf6d27N4cPH6Zw4cIsWbKEf//9lylTpuDt7Q2Yzftly5Zl4cKFlCxZkoCAAMqXL283rmXIkCHMnz+fFi1aMGjQIAICAvjjjz+4evUqS5cuzVQcSOvWralbty5Dhgzh2rVrlC1blmXLlqX5Iffx8aFBgwZMmDCB5ORk8ufPz8aNG21a1FKViC+//JLXX38dFxcXWrduTbNmzTAYDLRu3Zr+/fsTGxvLb7/9Rq5cuSzWvfSoX78+8+bNQ5Iki3tJp9NRp04dNmzYQKNGjayCN21RqVIldDod48ePJyoqCldXVxo3bmw37uRp0rBhQ/r378+4ceM4duwYzZo1w8XFhYsXL7J48WJ++OEHOnXqlKm5ZVl2+vszatQo1q9fT/369Xn33XctSnm5cuU4ceJEhtdu2bIlkyZN4tVXX6V79+7cv3+fqVOnUrx48TTzVa1alc2bNzNp0iTy5ctHkSJFqFmzJt9++y3btm2jZs2a9O3bl7JlyxIeHs6RI0fYvHkz4eHhGZbL3m9Qs2bNyJMnD3Xr1iV37tycPXuWn3/+mZYtW1q+zxoadnk6SVEvLv/884/43//+J0qXLi28vLyEwWAQxYsXF++//74IDg5OM37WrFmicuXKwtXVVfj7+4uGDRuKTZs2Wfb/+++/olatWsLd3V3ky5fPkrLNI2musbGxonv37sLPzy9NoamVK1eKsmXLCr1enyb19+jRo6JDhw4iR44cwtXVVQQFBYkuXbqILVu2WMakprqGhIQ4dQ0eLXbn7+8vvLy8xBtvvCHCwsKsxtordtenTx+RM2dOYTAYRIUKFdKkiAshxJ49e0TVqlWFwWDIULE7Pz8/4ebmJmrUqGFV7C4VnEyxFkKIsLAw0aNHD0uxux49etgsdnfr1i3Rvn174efnJ3x9fUXnzp3FnTt3bMo9ZswYkT9/fiHLslW69apVq8RLL70k3NzcROHChcX48ePFrFmz7KZkP8rp06ct6bIP8/XXXwtAfPXVV2mOeTTFWgghfvvtN1G0aFFLivCjxe4exdZ7bItHr4W9z13q5+vhc7aVYp3KjBkzRNWqVYW7u7vw9vYWFSpUEIMHDxZ37tyxOk9bsqemGD9aSC4VZ74/QgixY8cOy2c1O4rd/f7776JEiRLC1dVVlC5dWsyePdvmfOfOnRMNGjQQ7u7uaYrdBQcHi/fee08ULFhQuLi4iDx58ohXXnlFzJgxI93zTy0y+PBn3N5v0K+//ioaNGhguUbFihUTn332mYiKikr33DU0JCGyEFGnoZEJUouLHTx40BILpKGhoaGhkVG0mBgNDQ0NDQ2N5xJNidHQ0NDQ0NB4LtGUGA0NDQ0NDY3nEk2J0Xji9O7dGyGEFg+joaGh8ZTYuXMnrVu3Jl++fEiSZDMF/1G2b99OlSpVcHV1pXjx4syZM+exy5kemhKjoaGhoaHxghEXF0fFihWZOnWqU+OvXr1Ky5Ytefnllzl27Bgffvghb7/9dqbrGWUXWnaShoaGhobGC4wkSSxfvpx27drZHfP555+zdu1aqwKIr7/+OpGRkaxfv/4JSGmbF6rYnaqq3LlzB29v7+eqPLqGhoaGxpNHCEFMTAz58uXLUuNTRyQmJmI0GrNlLiFEmnubq6urVfuJzLJ37940bSiaN2/Ohx9+mOW5s8ILpcTcuXOHggULPm0xNDQ0NDSeI27evEmBAgWyfd7ExESKBHlx776S/mAn8PLyStPmYsSIEYwcOTLLc9+7d4/cuXNbbcudOzfR0dEkJCQ8tQbAL5QSk1rC+ubNm/j4+DxlaTQ0NDQ0nmWio6MpWLDgY2t/YDQauXdf4frhwvh4Z83SEx2jElT1Wpr7W3ZYYZ5lXiglJtXM5uPjoykxGhoaGhpO8bjDD7y8Jby8s7aGyuO9v+XJk4fg4GCrbcHBwfj4+Dw1Kwy8YEqMhoaGhobGs4YiVJQsptgoQs0eYexQu3Zt1q1bZ7Vt06ZN1K5d+7Gumx5airWGhoaGhsZTREVkyysjxMbGcuzYMY4dOwaYU6iPHTvGjRs3ABg6dCg9e/a0jB8wYABXrlxh8ODBnDt3jmnTprFo0SI++uijbLsOmUFTYjQ0NDQ0NF4wDh06ROXKlalcuTIAH3/8MZUrV2b48OEA3L1716LQABQpUoS1a9eyadMmKlasyPfff8/MmTNp3rz5U5E/lReqTkx0dDS+vr5ERUVpMTEaGhoaGg553PeM1PnvnC+QLYG9+UrdeuHub1pMjIaGhoaGxlNEEQIli/aErB7/vKK5kzQ0NDQ0NDSeSzRLjIaGhoaGxlMkM4G5tuZ4EdGUGA0NDQ0NjaeIikDRlJhMobmTNDQ0NDQ0NJ5LNEuMhsYLTmRIFKumbmDjn9uJCY8ld+FAWvVrxqv/exmDm8HhsZeOXWXV1PWc3nsBFxc9tVpVpWX/pgQWyPGEpNfQeP7R3EmZR0ux1tB4gbl96S4fNxhOZEg0qmKu+JlaYb1kteJM3DIcdy/bJcWXTl7DL5/8gU4vo5jMx8o6GReDnrFrv6Bio3JP5Bw0NB4XTyrF+sLZ3HhnMcU6JkalZJngF+7+prmTNDT+gyTGJ5GUkORwjBCCMV0mERX6QIExbze/Lh65wm+D/7J57LFtp/jlkz8ALAoMgKqoGJOSGdZ6HNFhMdlwJk8WYbqKGv0Namhb1NAOqDGTEcrdpy2WhoaGHTQlRkPjP4IQgk1/7qB/pU9p7fUmrTzf5L3qn7N94b/YMrie3X+Ry8euWSkhD6MqKuvnbCMuKi7NvqWT1yDrbf98CFWQlGBk/aytWTuhJ4xIWI4IbQHxc8F0FkynIO5XREgzRNLOpy2exn8YNZteLyKaEqOh8R9ACMHUQbOY0Ptnrp56UCr80tGrjO02hVlfzk9zzLl9F5Fkx51zkxOTuXryRprtR7eeQrWj/IBZkTm27VQGzuDpIpLPIKKGYL4VKA/tUQEjIuI9hHLv6Qin8Z9HSclOyurrRURTYjQ0/gMc2niclVPXA2YFIhU15f8XfLucU/+eszpG1sk487sn62z8TDgRSvc8hduJuD+x/3MogGRE/MInKNFDq6sRiNjpqCHNUYNrooZ1RsQvQwjjU5FHI/tRRPa8XkQ0JUZD4z/AqmnrbSsbKej0Mqunb7DaVrlJBacUjS1/70ozrny90g7Xk2SJCvXLpju3I66cuM6crxYw7cPZrJy6ntjItG6tbMO4C2sLzKOoKWOeLMJ0AxHaChH7AyhXQURA8glE9BBE+P8QIvGJy6Sh8SyhKTEaGs8AqqoSH5OAoji6kdrn0pGrVsG5j6KYVC4duWq1LahMAao1r+hQGQFYNXUDR7ectNrW8aNWdteTJHAx6Gnx9itOSm9NYnwSI9pPoH+lT1kwfjmrp29g6qBZdM3Xl41/bM/UnOnjRESBeLJRB0IIRORAUMOxli9FoUw+hIiZ/ERl0ng8aDExmUdTYjQ0niJhdyOY9uFs2vn1oq1vT9r49GRyv1+4ezU4Q/O4erimP8Yz7Zihf32Ap4/tFOpUdHrZ4qpKpfqrlek5sotlfyqyTkbnomf4kk/xz+XrjOgARARHsmraBv4as4RPGg1n7+pDgFn5MiUrCCEwJiYzsc9U9q874tScl45e5bu3ptGj2Hv0LD6QH9+byfUzN20PdqkO6BzMpgNDDafPJ1tIPgqmc9i3EKmQsBChxj9JqTQeAyoSShZfKo7j2/6raMXuNDSeEsHXQxhU50si70dZrBrGBCMb5mxjx5K9TNn1NYXLFXRqrvoda7Fo4kr71hFZon6HWmm2++TwxjfQh5gI+64axaRy8ciVNNt7DO9M5cblWfHTP5zZewG9QUetVtVoO/BV8hfP65Tcqqoy64u/WTJpNaoikHSSw4BhSZaYO2oxNV+r4nDe1b9s5Mf3fkOne1DDZt1vm1g7YxND/xpEo651ref17IlIWm9rqhQEkkd3p84p20g+ivk508EztogH00UwVHxSUmloPFNoSoyGxlNiSv9frRSYVBSTSkJMIt/2+JHphycgSek/YbV+pxkrf/6HxPgkq8BeMFtHPHzcafF2Y5vHunm5pTu/vcq95euVoXy9Mukeb485Xy1g4YSVlr+FyXGMjlAF5w9eIvR2GDnz264KfP7QZX587zcQ1jVsUv//2x4/UqJqUStFSzJUA+/PETHjMVtkUq0fOkAg+U5E0hfKzClmASefrCXNoP68owrzK6tzvIhon34NjafA3avBHNp43K7lRFVULh+7xoXDaS0gtshVMCfj1g/Dy88TAFkvW9w8PgFejN/4FX6Btt079drVdJhqLetk6ndMa8XJKtFhMSz+fnWmjk2ItR/QuuKndeh09l1DQsDq6RvTbJc830IKmA+uzUHOCXJucGuPlGMFknvrTMmZJQy1SDfSQfIFfaknIo7G4yOrrqTU14uIZonR0HgKXDtlJzbjEa6euE6pasWcGluuTinmXZ/O9gX/cnLXWZCg8ssVaNiltsMeSK/1fYXF368iITYB9ZE8TUmWMLi50PqdZk7JkBH2rDyIKdmU4eMMbi7kdNCb6djWUygm+wHSqqJybOtJm/skQ1UkQ9UMy/Q4kFzKIlyqpbiVbJ2PhOTZE0ly3N9KQ+O/jKbEaGg8BVzdnbvxGNxcMjSvu6cbLd56hRZvOZ8Z5J/bj/Ebv+KL174hOizGkq2kqioePu58vWoIuQrmzJAczhATEYcsyw6zqh5F1sk069UId08HLjBnHkidcNE9C0h+PyDCe4ByBfOJCSzuLtdXwfOdpyugRraQHZYUzRLzjDNu3DiWLVvGuXPncHd3p06dOowfP55SpTRTqsbzR7m6pfDw8SA+2n5mid5FR7XmlZ6IPKWqFzdbcRbu4di2kyCgQv0yNO5ez24DyKySr1juDCswuYMC6TW6q8NxVZu8xOa/dtptpyDrZKo2eSlDsj4tJF0g5FwBCesQiSvN6da6wkgeXcBQz6l4KY1nH1VIqCJr72VWj39eeW6UmB07dvDee+9RvXp1TCYTX3zxBc2aNePMmTN4eno+bfE0NBxiTDSyfeEets7fTUxYDAVK5aNBx1qsn227v5AkSbQa0AyfHN5PTEY3D1de7fMyr/Z5+YmsV+O1yvjm9CY6LCbdAsCuKbL1GNEZ35yOO/S2e/81Nv6xw/ZO6cG1fV6QJDfw6IDk0eFpi6Kh8czx3Cgx69dbpz/OmTOHXLlycfjwYRo0aPCUpNLQSJ+wuxF81ngkN8/fQZIlhCq4fNzceLFQmfzcOHsbnV5GCPMNVjEpNHq9Lv2/6/m0RX+suBhc+Pi3dxjZcSKSZN0uQdbJuHm6Mu6fLwnI609AHj+HcT0PU7xyET7+bQCT+v6CrJMsFhlZJyNJEl8u+Ii8RXM/lnPS0MgMmjsp8zw3SsyjREVFARAQEGB3TFJSEklJSZa/o6OjH7tcGhqPMqrjd9y5bG4emHqjTr2x3jh7m06ftEaWZUJvh+EX6EuTHg0oUaXoU5P3SVKnbXW+XT+M37/4mwuHLgNmRa76q5XoO6EHQWUKZGreV//XmNI1S7Bq6nqObT+FLMtUa1aR1u82d7qGjYbGk0JBRslisnDman0//0jieerSloKqqrRp04bIyEh2795td9zIkSMZNWpUmu1RUVH4+Dg2SWtoZAfnDlzk/VpfOBzjn9uX+Td/Rad3VDH2v8/dK8FEh8UQWDAHAXn8n7Y4GhpER0fj6+v72O4ZqfNvOVkIT++sKTFxMSqvVLjxwt3fnss6Me+99x6nTp1iwYIFDscNHTqUqKgoy+vmTefSWjU0sosjm0+m25soIjiKG+duPyGJnl3yFs1NqerFNQVGQ0PDaZ47d9LAgQNZs2YNO3fupEABx6ZmV1dXXF3T7ymjofG4UBXVqWzejGTpaGho/LfQYmIyz3OjxAgheP/991m+fDnbt2+nSJEiT1skjWeYkMRolt7cz+a7J0lQjBT3zkOnQjWpF1j6iaallq1d0m6qbyoePh4ULJXvCUmkoaHxrKEIGUVkMSbmuQsMyR6eGyXmvffe4++//2blypV4e3tz7545UNLX1xd398dTx0Lj+eRM1C0GHvydeJMRFfM3O8wYw97QC7TOX4Uvy3dAfkL9Zio1Lk+Bknm5cznYprVFkiVaD2jqdOaNhoaGhsYDnpuYmOnTpxMVFUWjRo3Imzev5bVw4cKnLZrGM4RRNfHx4T+sFBgANSV+ffXtIyy7eeCJySPLMiOXfYaXn6dVbExqr6KKDcvRc2SXJyaPhobGs4eKhIqcxZfmTnqmeQ6TqDSeAlvvnSLcGOdwzN/XdtOxYM0n5lYKKluQ305+z6ppG9j8107iouLJVywPrQc0o0mPBuhdHnwNFUVh0587WTX1H66dvonBzUC9DjXp9HFrCpcr+ETk1dDQeLJoMTGZ57lRYjQ0nOF4xHV0kowi7Meh3IoPJyo5Hj/Dk6v0HJDHn96jX6f36NftjlFMCmO6TuLf5QcsRfGSk0xsnruDLfN2MXrFYKq/WvmJyayhoaHxrPPcuJM0NJzBeePKs/fUsmraBvasOAhYV69VTKpZwekyifiYhKclnoaGxmMiNbA3q68XkRfzrDX+s1QNKOrQCiMBhT0D8XV5toLBhRAs/3EtAttuU6EKEuIS2TJv1xOWTEND43FjjonJ+utFRFNiNP5TNMxVltxuvsh2vtAC6FGkwTPX/Tc+Op67V+5jR4cBQKeTOX/g0pMTKpMIkYSIX4Qa1gn1fl3U0LaIuD8RquNYJQ0NDY2MosXEaPyn0Ms6plTtxbsHfyfSGG+xbKTGybweVJdW+as8ZSnTonNx5qsooXd5tlsTCDUWEd4LTCcx270EqKGImLEQPw8C5iHpcmb7urGRcWz8Yzu7l+0nITaR4pWL0PqdZpSsWizb19LQyG7UbOidpDp6AvoPoykxGv85innnYVG9j1h9+zCb750k3pRECe+8dCxUkyoBz2aRRDcPV8rWKcW5fRdQVds/RopJoXqLpxPYq6oqW+btYuXP/3DlxHVcDC7UbV+DTh+3puhLQZZxInoMmE6n/mX9r3IDEfU5UsDv2SrbtdM3+bTxSKJDYyxZjFdPXmf9rK28+VUneo3qmq3raWhkN9lT7O7FVGKeywaQmeVxN/PS0MgKe1cfYnjb8Tb3yTqZPIUDmXX2hyfeKFJRFL5980e2L9xjyZoC0OllQGLE0k+p3boaQg1H3K8HmBzOJ+XciKQvnGW5Qm+HceviXcZ2m0JUSLRVMPTDDFvwEQ271MnyehovHk+qAeTfx8rj4Z2173V8jEL3SqdeuPubFhOjofGMULt1NfpN7AkSlsJ4qUXxcuYPYNz6YU+l0/U/M7eyfeEeIG3WlKoofP36ZKLDYyD5BOkpMAAYj2RJnhvnbvNFy2/oVmgAnzUeRWRwlF0FRpYlFk1cmaX1NDQ0nl00d5KGxjNE509aU7t1Vdb8uomrJ6/j6uFKvfY1adilNq7uT6eZ6bIf1iBJYMtmKwQkJyaz+c+dtH/Hy7kJsxBTffP8bQbV/oKE2ESHQdCpqKrgwuErJMQm4O6VuYw0IQTHt5/m0IZjKCaVUtWLUbd9DVwMLpYxxqRkzuw5T1J8EkHlCpKncK5MraXxYqIICUVksdhdFo9/XtGUGA2NbCAmIpaEmAT8cvthcHVJ/wAHFCiZjwHf93JqbFx0PPtWHyYqJJrchQOp8Vplq5trVjEmGrl57o7DMZIE5w9dApdegAuQ7Gg0uNTItDy/fvonCbGJGe76ndku4SG3wviqzbdcPnbNbAWTQElW8Av0YeTywZStXZJFE1exYPxyYiMeZF9VbfoSH/zSj7xFcmdqXY0XCyUbAnsVLbBXQ0Mjo5zYeYa/Ri/m6NZTALh5uvLq/xrTY3hnfHJ4P7Z1hRAsHL+Cv8YsISnBiCxLqKrAJ4c3g6a+bTMGJCo0mnW/bWHbgt3ExyQQVK4gbQY0o8ZrVeymnMs62a4VxoIkoXPRIcl+CPeOkLAIsKU06MC1MZK+QKbOOfxeBAfWHc1QCxJJgvwl8+Hh45Hh9YxJyQxuMoq7V4IBc2B1KtFhMQxpPoZGXeuyftbWNMce3XqKQbW/ZNqh8QQWyGFz/tSMql1L9z3IqBrQjFLVi2dYVg2NFxVNidHQyCS7lu1nTJfvrRSAxLgkVk3bwIF1R/hx7zf45nw8AXYLx6/g9y/+tvydmtEUHR7D190mY3A3ULt1Ncv+62du8kmjkUSHx1jiR0JuhnFg7RGa9GjAZ7PfQ5bTPgnqXfRUfLk8J3acsWvNUBWVGintECSfoQjlGhj3YQ65Ux/8qy+F5PtNps/5/o3QDPdQEwI6ftgqU3WBdi3Zx60Ld23uU1WBMTHZpgID5msSEx7D/HHLGTT17TT7bWVUXTt1gw2zt/HGlx3pPcZ+ewqN/x6qkFGzmJ2kvjg5OlZogb0aGpkgMT6JiX2mIoRIc3NXFZV710KY89WCx7J2XHQ8f41ZYnunMIeczBzyl+XmqCgKX7YaR0xErFUAbKrcm+fuZMVP/9hdr+vgdnYVGEmWCMjrz0uNypr/ltyR/Gch+f0EhrqgKwaGGki+E5ByLEKSfTNxxma8A5yMueFBYHTTng15re8rmVpv55K9lsBqW6TnolJMKhvnbENJuoWIm40a8wMiYQXJSdEMbTGWmPBYK6VMMZnnmzd2KdsX/ms1lxACYbqMSD6JUCMzdT4azy6p7qSsvl5EXsyz1tBIwZRsIuRWGNFhMRk6bseiPSTEJNgNLlUVlU1/7iAhLjEbpLRm76pDJCUY7e4XAm6cvc3VkzcAOLDuKMHXQhzedJdOXoOq2t5frVlF3vvxf0iSlOamLlRB+N0IXs/fn6/afsulY1eRJD2SW3PkgN+RA/9BDvgTyb0dkmTIxNk+IH/xvBSrVNihYgHgFeDJSw3LMmLpp3YtTI4QajhCuUd8dKzdrCdn0OkFfYdfgYhXEDHjIe5XRNRgREg9yle75FAxXDRx1QN5EtYhQl9FhLZAhHVE3K+DGvkJQrmXadk0NP4raO4kjReShLhE5n+zjDW/bCQmJSCzTM0SvDGsIzVbVk33+Btnb6N30WFKVuyOSUowEnorjIKl8meb3ADRoTFW9VrsjktRzE7sOIPORYfiQNb7N0IJvR1OroJpq+kqikLEvUhcXF0wJtpWnoQqOLDuKIc3nWDi5hGUq1MqA2fkPG99050vW46zvVOCLp+0oe+EHunOc3b/RZb/uJajW04hhKDSy+XoOdSXAvlXgeksACN+9WDpL74s/DknycaMP++9M+Y2LXuEpSRjCVLjhHS6RIb8fIO4aB0Ht6Z1NwpVcPGIOaPKTVqKiBmNdUqXCRLXIYwHIMdSJJ2WCfW8o5L17KLMha4//2iWGI0XjoS4RD59eSQLJ6y0KDAA5w5eYljrb1n9y8Z053D3crNbWffRcdmNp7+HUxaCXIXMCklW20RN6f8r88cts6vApKIqKorRZHGzPQ6qv1qZYQs/wtvfE0gpuCeBTq+j62dt+d+47unOsebXTQyq8wU7F+8l8n4UUSHR5M2zkgK5xyOM5yzjPLzieeOju4yZexW9S8ZuEYH5jLTsEYYtI5AkgSqg52eOLSmKKQIRkxpD9Oj1VMztHGJ/ypBcGs8mKnK2vF5EXsyz1nihWTppDRePXEljzk9VDH5+/3fC7kY4nKNeh5oO3TOSLFGialFy5redmZIVTmw/k+6YsnVKka9YHgAqNCjr0AoDkDsokJz5A9Jsv3T0KutnbXOcnfQQqiq4ffEup3afS39wJmnQqTYL7vzGV4s+ps/X3fnol/4suP0rb3/7Jjqd42KAV05c54d3Z4B4EIOSv2gSvQebA3gl2fpEZR1UrBvLa2+GZ0jG+q2i02nmCSUrJpC7YFKafZIEBUrlw0O/EXD0vimQsAIhst9lqaHxvKC5kzReKIQQrJq+waElQwjBhtnb6P5FB7tjCpcrSJ221dm35rBNZUaogp4jumRZ3mRjMuF3I3HzdMU3pw9x0fFsW7A73ePqtHmQmVTjtcrkKZKL+zdC7SpeHT8yZ/Ac33GazXN3EhUaTWCBHMSEx6LTy5YbvrPsWLyHyPtRFK0YRP7ieTN0rDMYXF1o0Kl2ho9bNXU9Op31+bR4IwxVBZ2dRzpJgnZ9I1g12/nGlbkKuqKqZiXIEV6+CsE3rbelZlSh7gN0OK6CnARqGOiy12Wp8WTJnt5JL6ZNQlNiNF4oEmITibgX6XCMJEncOHcr3bmG/DWIcd1/YO/qQ+j0MpIkoZhU9AYd7//8NrVapR9bY4+4qDj+GrOUdTM3Ex+dAEDZ2iVp3L0+yUmOS/vLehljwoOCczqdjq/XDOXTl0cSFRpt1ftIMak0692IV99qzLDW4ziw7qhle2aUl1RW/ryelT+vB6BS4/J8/NuAZ6Lw24ldZ9KcU6ESiQ6VDUmCfEEJTs3f+I36tH3vVc7vmoxOf8XhWMUEoXcfuBtlnYyqqA8yquLOkH5ZYgkk57O2NJ5NVCTUrJSyTpnjRURTYjSeWUyqwq77ZzkacQ2Ayv6FqZ+rDPr0Hm8d4OKqtxSGs4ckSbh7ph/L4u7pxuiVn3Pp2FV2Lt5LfHQCBUrmo/Eb9fAJyHyhu7joeD6s/xU3zt62spyc23+RM3svpD+BAFcP60ygoDIF+P30ZP75fau52F10AoXLF6T1gGZUa16J796axsH1x4AHbpbMKjCPcmLHGT6oO4zphyeQI69/tswp1ChQboHkAbrCTteBkW24mxLidKgK6Bz+Ggq8fBVio6yP98uZTMseYdR9LQpXd5UchXLikasOE3sqNGkn4+6p2oyLEQJiogwM/K4Qf3wTR0yEiaIVC9Pm3ebUbVcDSZIQ7i0RcVMdyKQDQ+0spa1rPBtolpjMoykxGs8kl2Lu8dHhPwhOjEIvmb+cC67vIbebL5Or9qK4d55MzeticKFmq6rsX3vErmtFMSnU71jL6TmLVypC8UpFMiWPLRaMW55GgQGsFC9HVXRVRaVO2+pptvvk8Kbr4LZ0HdzWanvonXA2z92ZpXRiR6iKSlRINIu/W+V0OwV7CCUYETMREtdhcbPoioL3B0huLR6MEwLUYBAJoMuLJJmV0hqvVuLmOetr++86Xxq1jXS4riRBx/73+WNiHkjJIilVKZ5vFlzG3VPlgW60HRG+hQ79i/DDZwUYOu0GqmLtVhLCPJ+Pv5EGzdfS4LUCSAFzkR5xCUn64gi3linn+uh7Y5ZB8nrfiaumofHf5cVU3TSeaSKMsbxzYCYhidEAmISKSZhvOiGJ0bxzYCaRxjhHUzik29AOZiu8jYd3WSdTqnpxKjUun+n5s4JiUljz6ybHhdQcKDCyTqZex5oUKJnP6TUPbzye6d5CYA5iTs8Qoioq//y+hcvHr3Hp2FWSEtIGtKaHUEIQYZ0hcS1WcSLKVUTkB4j4eeZxiRsQYW0QIQ0Qoc0R92uiRn+NUGNo/U5zcyG8h+Tdvc6XGxddMaXTgLvr+/dp3tUc4OvhpTJm7hXcPR5WYCA1ELflG1dBkhn2ZhEunbJuPJl6rSwWGuUuIqK/zYwuyXc8uLXDLLCM5blT8kPyn45kqOxYaI3nAq3YXeZ5Mc9a45lmxc1DRCcnoNqIB1ARRCcnsPLWoUzPX6ZmCUYs/hQ3TzeQQO+iMzf3S9k3du3QDBdIyy6iw2KIjXSsoOn0OsrULIGsk5F1Mjr9A/mrNavI4DkDM7SmyZjO3TsdhCoILJQz3e7U8dEJDKj8Ge9UGUyXvH2ZOeSvdNO2rdaJ/QnUENJm7Jg/JyJ6LGrsr4jI98H0kNtNJED8PER4N/IU9mT44k8sFX0BVEViwCulmPtdHhQF7J2ITgcffX+LH9Ze4N2v7+IToNh1QQkh07H/fQ7v8OX9FiW4d9MFYVdPVMzyGvcCkBCbwJpfN/H165P4+vWfWflnQ+Jc1iB5D0HyGojk9xNSrl1Iro3SuWIazwuqkLLl9SKiuZM0njk23zuBcBDQKBBsvHucXkUbZnqNOm2rs/DODLbN/5crJ67j6m6gTrsalK1dMlN9dqzkEyqmZIXdyw5wcucZhBBUaFCW+h1rptth2tXD1YkFzCnUw5d+ysY527l7+R6efp68/HrdTDUPLF6laIaPeRhzQKpAp9NZNUl0RHx0Aou+W8WFw1f4Zt0X6F0c/xQJkQgJy0k35Th2UuoRafeZLiHiZhAVUglVUc1xJ6mtGUwSC37Kza2b9fhi6mF0XLW5giRB6coJlK6cgDlzyLY8kqRSqlI87j46vLyN5CnoqLM3gB5h3MO5Y4F82fIbYiJiLZ/DXUv2MXuYK6NWDKZy4wrpzKOh8WKhKTEaTqMKlV33z7H85gFuJ4Tj6+LJa/kq0SJfZdz1WSsp/zBxpvRdDc6MSQ93L3de69sky/MACOUeIm4WJCzj8kkjw3oUIzxYby7GhsSaXzfx6yd+jF37BcUr24+f8fB2p2rTlzi69ZTDmJ0GnWqRM1+AwzRwZylVrRglqhTh8vHrmXIrqYpKTFiM0wpMKkIVHN1ykm3z/6Vpz3QUUiUUcOY9dxTXoxJyaTGT++8HQVpFWcDu5RfZ+HJpWnS6gWOFyTlFd865H9i9eDWQfm2fpLh4hr76NQkxiWb5HnIvJcYnMazVt8w8PemZyPLKLoRIBmEEySPLDw/PM2o2uIO0YncaGg5IVk18duQvPjv6F/vCLnI9LpSTkdf59sxK3tzzE6Ep8SvZQXHvPOgk+x9NnSRnOrD3cSBM1xChbSF+LpEh8XzepRiRoWb3jmJSLTf3yJBoPntlFBHBkQ7ne2NYJ/MNzE7MTsVGZSlTq2S2nsOQvz7Ay8/Tys2Sup7BzbH1CEBv0PNyt3oZvhFJssSaX9OvkIzsTLaXwJgoceuyfYV6/TzHz22SLLFyRjKOFRgd6CulM0YGfTn8c+ei1Xu9QE6v6KGJfRtV4mMSbPawEqrAlGxi1dQN6czzfCCST6BGvIsIroC4XxkRUhcROxWhxj9t0Z4KqV2ss/p6EXkxz1ojw/x2aSu7Q84DD1q+pz4n3kmI4Ivj87NtrY4Fa6LYDyBAESodC9bMtvWyioj6FEQ0oLBuXgCx0TpUJe3NXFVU4qPjWTtjs8P5KtQvw7AFH+Hq7vpQzI75q1qxUTlGLhuc7U+thUrn55ejE2n/fgu8/Mwl/f1z+9JtaHu6f9nR4Xo6vUzNllUYPOe9DHeMFqrg9qX0GxlKsq+5KzaO0+t1LoLrF9z4qE1xblxM65q7fNrdobVJqIJrZ0IRLvWw/fMoAwbwHQ6Sj50xACqS5//Mskt6JI+e2LfeyMTH+TCh3xmHGWKqovLvigN29z8viMStiLCukLQNS8eflBYKIrzHC6vIaGQOTYnRSJdEJZnF1/fajVNRhMqxiOtciL6TLevVylmCNvnNheIe/tlP/f82+atSK2eJbFkrq4jkM5B8gtSn8l1r/BwEcJrTpHcs2pPuvAVL5eP1Ie2o07oaNV6rTKdP2vDz/nFM2DTcomRkN4EFcjBgUm+Wh89hg2khi+7OpPfo12nzbnM8fN3tBjurqqDDh63Qu+jJkTcgw72anD0fczqx4zRwnQ7qNI8mIlTPR22Kc/f6w1YZHS7uudIN2ta76JD9fwbX5pg/dRIW5UnOgxTwJ7JLOST/GSC5Yf0zmjLOow+4tXqw2fNtMKS6zKzHJyXpGdwxNyZj+inuGQmEfhYRajwi6hNSWh4+slcF02lE3LSnINnTRUHKlteLiBYTo5Eul2LuEac4jkeQkTgUfoWSPs6n9tpDkiS+KN+e0r75mXd1N7cTzGmt+dwDeKNIPToUrOHQMmBKNhF8PQRZJ5M7KPDxZholn7X6MzHOHAPjiMQ487UMvxdB5P1o/PP44Z/LXLAsOiyGsd2ncGTTCWSdjCSZXVL5Tt2kUdc6j+UUbPHwNfP29+Lb9cMY8urXxEclWGI1Ul1Pg+cMpFS1YgBs/HO7032WzOtINHmzgVNjJUMVhNd7kE7TQ0mGctXj2LbMn78m5aZmk2hWzsrJpZMeCFSb7ppUdHqZ2m2qIckeSP4/IEw3IGk7iCRwKQ2Gukgprk7JUAVyrkfEL4DE9SljyiJ5vGEuQvfQZ1SSXMB/GiSuMaeCm66A5EFUTEPea3SSkNvpx5Tp9DIlU67zc0viWhCOsu9UiF+A8PrAfM1eELLDHfSiupM0JUYjXRxlClmNy8ZaabIk06lQLToWrEmYMRaAHAYvh8pLsjGZheNXsuKndUSFxgCQu3AgXT9rS6sBzR5P4OAjP7RFyiYQfNOAYsOdBOaWAIGFcjC46WiObjmZMgfUaFGZPmO6ManvL1w+fg3Ayu1x71oInzYeyYxj35GrUGD2n0c6lK5RgrmXp7Lpzx0c/OcoJpNCmZolaNmvKbmDHsgTHRbj9JySLOGT05vqzfMTenkafjld0LmVBEM9JMm220hyqeDUp1GSQFEktizxZ/PiAGRZIAQElYrm9hVXko2pFpaHDzJ/hjt/0ubBJn0h0Pe0v44uD5L3h+D9oRMy6cG9HZJ7O8u2v8fOIjz4Iha3igMUk0rb915Nd9yzjDCdw3zbcZDWL6JBDQVd9vfc0vjvoSkxGulS3CsP7joDCYp9U7aKoHJA4WxfW5IkcrqmH9SpmBRGtp/IwfXHrLI6gq+H8ON7M7l+9hYDf3wr2+V7EKdhNo03eCOGf9f52R2umlTO7LlgbQ0QcGjDcY5uOWm3L5KqqCTEJLJsyloGTOqdZbGNSckkG024eRjS7fycire/Fx0+aEmHD1raHeOX08fS6yk9fAJceeuLixTN1wtZBjXWXNJFSLmR/b9HMtRIe5DLS6R7EwROHzS7qISQAEGz18N546NgcuVP5vZVA0O6FuP+LQOyTrZ8XnR6HUP+fJ/SNdK6Ko1Jyexetp8ze84jyRKVX6lAzZZVnL529gi5GYriZEZYp49bU6XJS1la76kjuZF+PyhAcqLUwH8IBbLsDspYbuB/B02J0UgXd72B9gVrMP/avzatMjpJppRPPsr6FngK0pnZ+vduDvxzNO2OFHFX/ryel1+vR7k6pbJ1XUmXA+HeCRG/mFnBpfjdrzSurUIxrIlGSCClrJ/aJsDgbsCYkFYZVBU13fRmVVHZvXwr/Sc0ANkPSQ7IkKyKSWHZD2tZ8O1yosNiLdsrvlyOAd/1cpj6/SjxMQkcWHeEmPBY8hTJRZWmLxETHkvo3Qinjncx6On31QUad4iwlORPVQeEch81vA9yjkVILuWsjpPkAIRbG0hcgS3rhckER3d5c+fqg5tgtw/u0/vze6TqjfmLGJm1+xy71viyb1MOMDSkRNUyNO/TCL/AtH2Izh24yFdtxhN5Pwqdi1nKFT/9Q96iuRm7digFS2W+g7RPDp80XbUfRZIlvvz7Qxp0rv3cpyFLrq8g4n5zMEIGlwoZ/mw/72jupMyjKTEaTvFOiaZciL7DofAryEiWaroSEOjqw7hK3Z6qfKumb0CSJbvZHTq9zNoZm7JdiQGQfIax8E40v93zBAkS3smFqZgbrksj0N0yFznzKuxLufIl2Lc6c5WG8xRKotdn92jQJhIRanYpCEMdJK8PkQyV0j1eMSkMa/Mth1KaPD7M8W2nebf654z750tKVS/O4U0nSIpPonD5gpSsah2DIYRg4YSV/DVmCUnxD+KkcuTzp0qTl0hOSq+oW+r5xNKks22FR5YFqqogYn9G8p/+yPoKeL4FpjOI5HOoqjmYV1XNn8W711z57oOClvGB+Y30/OxeyrwP5nExCBp3iKRR2yhk7/rIPtb9pFK5fzOUwU1HkxRvVjyV5AfPu8HXQ/i08ShmnZ2Cp4+HU+f9KK+8WZ9/ft9id7+kk2gzoDkNuzy5eKjHiktlcKkKycewbTtQkTzfecJCPX20BpCZR1NiNJzCVefCj9X6sPneSZbfPMCt+HD8DB68lq8KbQpUxdvFPf1JHiO3zt9xmJ6qmFRunL31WNY2CR2/38kJpLhRJInk5r4kN/NBilVBQKKvgZO9zjqcxx75Cifxw5qLePgo6B/+xhr3IcK7g/9vSK51Hc6x5tdNNhWYVIQq+OK1b5BkUJIfWAWKVSrM53++T5HyhQD4+5tlzPlqQZrjw+9GsunPHemeiyRLlK1dkmp1d6KY7HeOlmUVkrYi1Fgk2ctcFC1uJiJ+rjleApDkHMRGKCQbY4m478KGBQFsWuRPYrzOotA26xJuzhaz8/su6wQkLEF4DzbHrDzCqqnrSYo32rSSqYpK+L0INv25g3YDW6TZ7wwvNShLtWYVObL5RJrO6rJOxsPbnc6ftrFz9POHJEngPxUR0R+Sj2O+BT24tpLPV0hujZ+afC8aU6dOZeLEidy7d4+KFSvy008/UaOGDTduClOmTGH69OncuHGDnDlz0qlTJ8aNG4ebm9sTlNoaTYnRcBq9rOPVfJV4NV+lpy1KGty93Rz2HJIkCU/fx5OafCziOtHJNuJAJAnhbXY/JAmFqAAF/f2Mz//OmNt4+tjq06MCAhE1BAK32w2GBVj+w9p011EVNc3D8dWTN/io/ldMPfgtPjm8mff1EpvH2mpeaHOcKogJj8XLL9lsRXE8GkQMQrghIt4D4w6s4ilEOL4BglNH6vFF5ySSHnLTFX0pCHcvN/IG3Uw/AkPEgogByT/Nru0L96Tr5tu5ZG+mlRhJkhix7DOm9P+VrX/vRghhaYdQsFQ+vlzwkVXg9PNIdHgMu5bsIyI4ipz5A6jfqRYeAYvAuAeRuAFEPJK+GLh3QNL9d6oRZwSBhJrFmBiRweMXLlzIxx9/zC+//ELNmjWZMmUKzZs35/z58+TKlSvN+L///pshQ4Ywa9Ys6tSpw4ULF+jduzeSJDFp0iQbKzwZNCVG4z9B4271WPz9ars3HCHEY0tRjneyBYKazwXOJToco3fRYXrIZZEzr5HqL8dgv4CxADUYjLvATkPAZGOyUwXlbMqsqCTGJTL/m2WUq1ua5Cw2iwSIvB/N/ZuullgY+xhADoCEVWDcbmO/WT0pX2U3i2/P59CmSyREHCSopImS1Qpz/EAFLu7c6kQcqQ4k2wpuYpzj9wuB04HM9nDzcGXI3EH875vuHFp/DGNSMsUrF6FcnVLPdQyMEIK/v1nGX2OWoCQryHoZxaTw8/u/8/b4N2k3sEW6FsQXhafhTpo0aRJ9+/alT58+APzyyy+sXbuWWbNmMWTIkDTj9+zZQ926denevTsAhQsXplu3buzfvz9LcmeVF9OJpvGfo+3AFrh7u6Upmw/meJg8RXLxcrd6j2XtIM+cTo2T76YfL9JqQDP6f98T/zx+AOQrbHSgwFhmBtM1u3t1erN7JbMoJpUtf+8m/F4kOhvXN3fBJCrVi6F4hXicyTyJDoth8xI/h0NVVQb39kiSq7muisOfKh2uxo+pW/8rGrVYS/FSGxCxP/FS2b7kLloRvcNyIzpwbY4k2a7TUrhCIZufKcvRepmiLwU5WsBpchXMyWt9m9BuYAvK1y39XCswAEu+X82crxZgMpoQQpjjiQQkJRiZOmgW62dtfdoi/ieJjo62eiUlpX3IMhqNHD58mCZNHvSOk2WZJk2asHfvXpvz1qlTh8OHD3PggLlq9JUrV1i3bh2vvfba4zkRJ9GUGI3/BIEFcvDdlpHkyGd2CehcdOj05kf9oLIF+X7bSNyc6RCdCQp75eIlvyBkOzcdGYkgz0CK63M5rIPn7u2OhMSfIxcRcS8SvauektXTT6kVQkVg31UmyzI1X6uS7jyOMBlN6PQ6qyyawqUTGL/oMn/uP8f4RVdo2TMsReFKX5GJCHFh9rfmOiCP1p5TTIDsj+T1XsqGKziqoxITCX98K+hSoSwtg8rTtngZfhySl7vXXajXbDOJSSVRVVsXXgb0SF72A0nbvNPcoTtJMam0GtDM7v4XlYS4ROaOXuxwzKxh8zPcNPS/iiqkbHkBFCxYEF9fX8tr3LhxadYLDQ1FURRy57Z23+XOnZt792xbbbt3787o0aOpV68eLi4uFCtWjEaNGvHFF19k/wXJAJo7SeM/Q/HKRZh7eSr71x7hzN7zyDqZqk0r8lLDstn+VJsQm0BMRBw+Obxx83BlaLl29N3/CwlKslXfJ50ko5dkRlToBFNj+eyVUeZ06keDkCXzU/3KaestN01TkokV06/SrqeBnHmNdsv5KyYY3/s4g/9sY+63ZIOug9uyb+1hp0p02EKSJWZ98bflb72LSpf37lOhljlV+8ReT34YXACE89d58bRcRIXrePPjYHIXMFupVAXikmqxb31z9q//CyEE5SsH0rRTIt5+aW94kWE6PmpTgns3DJZ+VUkJOv6Zl4Nty/35buklir0UB4bWkLg65SgZUEDOheQ3CcnFfsZavQ41adS1DtsX7bG6dqlxK50/aU3ZLDTjjImIZctfu7hx9hbuXm7U61iL0jWKP/dWmMMbj5MQ69gVF3EvklP/nqNiw3IOx70IKNnQxTr1+Js3b+Lj42PZ7uqaPQ9v27dv55tvvmHatGnUrFmTS5cu8cEHHzBmzBi++uqrbFkjM2hKjMZ/Cp1eR5221anTtvpjmf/qqRv8OXIRe1YcQFUFehcdL3erR48RnZlT+z1+vbiZrcGnUISKjET9wNK8XbQyxeR/uOd1gi9/d2XxVMGZ/Q/iKAqXL4jBzYVLR6+leeo3JQv+mJCHT6fcsCmPqsLK33Oya/kFfD7+gw+m93voWBNhdyJwcdVTrm5phvw5iPG9fnKYxWUXYR28a0qWmPB+ECajTPNu4SyenstcsM7hg7XgUVPUxgU52LQwgGLlE+jyWTNyBVVmaKuZJMautIzZs8KXP8d7MXLOVSrVtQ7e/mV4fisFJhVVkUiMlxk7IIjfd51H9voNvD+CxK0gEsHFcWXgVGRZZshfgyhTqyTLpqwl+HoIAAVL56PLZ21p1quRw+MdsWnuDqb0/5XkJBOyXgYBi75bReVXKjBiyScOA9EVk8L+tUfYtvBfYiNiyV88L6/1bZJtrq2sEhvhqLXAA+IitWaP2Y2Pj4+VEmOLnDlzotPpCA4OttoeHBxMnjx5bB7z1Vdf0aNHD95++20AKlSoQFxcHP369ePLL798vO1dHKApMRoaTnLuwEU+fXkkyUaTxZJiSlbY8vcu9q05zI97xjK20uvEmhKJNMbho3fH2/gHakxHhKqSM0AiVyNBnSawek4ONi6vxzuT3yJHXn96Fh9o10qyaZE/nt4m3v7qLjq9QDFJyLJAlmHNHzmYOTYfQhWsn7WVPl93w9XDwPxvlrNq+gZiws2WkmIVg+j2RUcW3p7BwJpDuX8jNEPnnjb7yFwJd+qwfNRrFcnh7d42O3cDuHspJMbLCJsuHXNV3atnvdizVmXv6hkYE41p1ktMkBneoyi/7TxnsdpEhunYscrP7rqqInH7ihsn93lSsVUCkr44wqMrJG5CxC+E2BkIfREkjy5ILvbddjqdjg4ftKTd+y2IColGkiV8c/pkyVpyaONxJvT+2fKeP1x/5vj204zq/D0TNg63eWx0WAxDmn/NxSNXkHUyqqJydMtJVk5dT6ePW9NvYo+nbsnJW8y5LKO8RdNmwbyIPOwOysoczmIwGKhatSpbtmyhXbt25uNVlS1btjBw4ECbx8THx6dRVFIrVjubnfg40JQYDQ0nEEIwvudPJBuTURXrL6xqUomLiueHd35j4pYReOnd8NK7IeL/RsRORpJA0qXUJEmhZc8wEhP2MuNTF17/vH26bp4VvweyeYk/5WvGce+6geDbBoyJEorpwY+KKVnhyJaTrPhpHWf3XrByWV05eYOvu06i7/g3mX5kAl+1/pYzey+g05vL7gtBJiw0EkkJMjtX+9m1wFRvHM3BrY6fCsEcW3LuwEWSk5JtyiFUieRkWPtnDv73hdlnf/Oim10FxiKhLLh0ypOKbQshlFBERG8wXcDsUlIh+QgiYRHCvQeSzzCrm78QAkyXQA0DXR5kfWH8c/uley6p3L0SzOa/dhIRHEWOfP407dHA0vdq3tdLkCUZ1UbLc1VRObr5JOcPXbY01nyYMV0mpemvlRqrtGTSavIWzU2bd5s7LWcqcdHxbJ67k72rDmJMTKZElaK0GtA0UxWJK9QvQ54iuQi+HmLz/ZR1MsUqFaZIhWfDcvS0UZFRs+hOyujxH3/8Mb169aJatWrUqFGDKVOmEBcXZ8lW6tmzJ/nz57fE1LRu3ZpJkyZRuXJlizvpq6++onXr1lluv5EVNCVGQ8MJTv97jlsX7trdryoqx7adYs+qg9RpUx0hkhEOui3LMrR7K4SFP5/l+plqTskQG6Vn38a0ZfEfZv/aw5zZeyHNjSP179+G/EWpGsUpUDIv5w9dQklWzbE2kpRqXMkQOr0g+KaBouUSuHLG3cra4uGt0KhthFNKjCRJxITHOQyiVRWJPRuLWpQYF0P6wgoVtiwL4sDOH8hf6BQtXr9N8QrwIFA4RftKmAv6IPA0N3sUSbsQMeNTFJ6UuVwqIXl/iWSo6HBNVVWZ/tEcVvz8D7IsWwrv/TF8IV0Ht6XTJ605tfucwzl0epndS/elUWIuHbvKsW2nHB67YPxyWg1omiHz/tVTNxjcZDSRIVHmj4GA03vOs+zHtbwzqbfDflm2kGWZj38bwNBXx6Z0Dn/wXsk6Gb1BzwfT+mZoTo3spWvXroSEhDB8+HDu3btHpUqVWL9+vSXY98aNG1afoWHDzEr+sGHDuH37NoGBgbRu3ZqxY8c+rVMANCVGQ8Mpbpy97dS4Ee0mULpGcYb+0YQ8vmEOx7oYBDWbxnLtzE08fNwd1htJDSRNj+PbTju0qMiyzNAWY1FNiuXpXYjU/2QcVZXw8TfRvm8o331QyGpf4/YReNkIxrWFEAJXD1fiohzHSBiND1Khi1eIxyfARHS4o58xiSsnk1HVk5zQCdbMLk77viH0H3knTaC0iJsJHm9A0g5EpI2MpeQTiPA3IOAvh60e/hy5iBU//QOkLSC4YPwKhxlqFqkliYTYRIQQnN5znn9mbuHO5XvEhMc6bK8BEHIzjJvn7xBUxrleZsZEI0OajTF3IBcP9NhUhXL6R3MoVKYA1Zo5Vt4epXLjCny3bSS/D51npbRVerkcfcf3yFCvrv86ipBQsuhOyszxAwcOtOs+2r59u9Xfer2eESNGMGLEiMyI99jQlBiN5wIhBAfWHWHl1PVcOXEdVw9XGnauTZt3m5Mzf47Hvr6bl/NltS8cucInr8xl2gYdvjns38SFCm5uJsKjkujyaVvmDE9bzh/MmUEePu7pBkHKOpmQW44VJ2caTWaU+q2iyJk3meP/erFpUUBK7yOJAsWSKFcjDg8vhfhY++ZmSRIIIZGclIysl1HtNEPU6WXK1CwN0mEQMehdoOvA+/w2Op9D+VKtAEqK62n5b4HkDUqi7f8euVbqPYTpEkSnxqI8qiiogAkRPQop53Kba8VFx7Pk+9WWvwPzGalYNxZZJzh/xJPrF9xYPX0j7t5uJMTYz94xmRQKlsnPxP9NZdMfO9DpzU0inVVmTRkoSrhj0V7C70Xa3S/rZBZ/tzLDSgxA+bqlmbxzDMHXQ4i8b3arPYnv6/PGk46J+S+h1YnReCokKsnciY+wXa7/EYQQTO7/K8Naf8vhTScIuxPBnUv3WDhhJf8r+yFn91987PJWf7USLq7O6fyqSSX8Xjxr5zr+sZZkuHnFnbxFc9Pti/aWOAadXkbWyZY6N/U71nIqi0NVVFwMT/K5xNybKDBfMkKFjyfd5LMfrxOQOxkQxMfKuHuovD4o2OEsLq5mpSUmItauAgPmuI+277UwW0tSfro69g+hfV9zxpBOJ5AkYRV7ZE/uRVNzodjSL41HQL2Pfb+aCqbTiOQLNvce2XSCpAQj7p4KQ6Ze588DZ/nsh5t8MukWM7afZ8KSS3h4RlK1aUX7RfQkcHUzEHHvQT+qB1az9BUYN09X8pfIm+64VA5vOu6woJ+qqBzdegrF5gVzjtxBgZSqXlxTYOwgUrpYZ+UltAaQGhqPn9CkGH67tIV1t4+QpJqfFmvkKE7f4q9Q0d92kN8/M7fwz0xzp9+HrQiqopIUZ+Sr1t/y943pGNxsV13NDrz9vWg/6DUWf7fKKc+Lqgo2Ls7H64NCzc0MH0FRIPiGgeO7PRg0owmyLPP+z2/T5t3mbJyznZDbYfgF+tKkRwOKVSzMzsW2q2g+SrUWlTiw9ohVUbrHh8TGRQFEhrqQJyiJuCgdezf6EhdtVr6uXiyN3uU+Xd4LIS5Gx6KpuZAwBzirioQAGrWJZNuKlJ5F6VzXN4Z1pHy9MghRDJF8Aox7kCSJAaPu0OKNMNbPz0HwTQ9C75fkwhFHDUElQu8auHXZlaCSD1UzlTyx3VnZBsptc5r2IyTGJSHrBF/Pu0KZKvE8GpZSrkYc36+4xLmL/bl5/g43z922+kzLOhmhCj78tR/TPpyZ4RglWSfT4q1XMlTYUTEp6boThSrM1/MxxG8KoQDCZgNODY30eDFVN42nQmhiNH32TmPVrUMWBQbgUNhlBhz4jV330wY7CiFYPGm13UJvqqoSFRrNjkXO3eSzwv++6U7L/s5XZ40Od0WSvS1ujFQUEyjJEhM/KES3oR0oVPpB9kdQ2YL0ndCDL+Z9yLtT+lCyajF0eh1B5Qqmu56Hrwc9h3dGSgkmfRQ5C60H7CFUiUPbvQm5bSBfkSTa9AmlaLkEcuYPYNiSGeDaCEmS+d/Qe/x18Ax9ht6lVa8wen1+lz/2nSUi1Pkb10sNygKYWwR4vAlyblLv8kElk+g/Smb4ksHUbFnBqXM1GR8eI4NHNySdk80W5bTNIgEKlclPzabRlK8Rb7NDt14POfMmU7HWSX7YPYYun75KUGkd3n7m78NLDcsycctQ8uedS0x4On2bHkYyx9EUr1yEPl+/7vxxQOkaJdLVldy93fl3+QFMyVnvnZWKSNqNGt4bEVwWEVwWNbQtImHZU03XfVooSNnyehGRxAv0iYmOjsbX15eoqKh0iwFpZD8jTyxiw90TVhVtU5EAbxd31r08FIP84Nc/NjKO9gG9Hc6r0+to3rsRH80YkCF5rseGsOTmPg6GXUYIqJGzGJ0K1iLIy/GN7M7lewxvN4EbZ27Z/cGVJHPfnV8PfUhyxBQk41p0OhVVhb0bfFg3vwz1OvXktb5N0tT0EEJwZu8Fti/8l9jIOPIVzYN/Xj9+GDDDoVx9vn6d7l905MjmE4zu8n0aF1TuoECiQqJJjHeuYaUzlKsRy7BfrxOQ24Qp2XzeOj3cu1OCvJXmAS6IqI8haRuqao7r0OshKVFi0icF2bHStjLwKLJOplHXOgz96wNE4j+IyA9T9qS9/ge3ejPszaIO53PzUFh44jRuHikbXCojBcw2z3i/DggHxdp0+ZFybkGy0dRKCMGRpQ15qdY9m0qMeQxIuvzg9gokLAZhdqkqclX0vh8gEjdxZtcyPmpT3OE5IIHBzYCSbCJ3UC5aDWhG63eaZbi9RnR4DN0KDiA50WjXIJMai1OmVgnGrR+Gp4+H7YFOIuL+RMR8jdm0k2r9Skl7d+uA5Dvuqde6gcd/z0idv8/2Lhi8smZJNsYamd1o0Qt3f9PsdxpPhJjkBDbaUWDAfCuKTk5gR/AZmuZ9UHjM2d+xjP7gbbhznBEnFiFJkkWmG/GhLL6+j7eLN+Zi9F0uxNzDXWegVf4qdAqqZVGu8hXLwzuTejGk+dcO12jZtymSviCGwO8R6hgS4+5y92oiQbX9GNsrr80U2ITYBEZ3/p5DG46j0+ssSpJQBWVqleTsPtuxGNWaV6Tr5+0AyF8iL+6ebuZMn9SbkgTB10PIVSgniTeyR4kpWDyRcfOvoE9JdX640WLufFcQEX2RAhYi+/+KSD5HQvAy1s9Zw82Lbmxf4UdCnPO+CVVRCb0djhBGRNQIHPlZqjSMIVcBI6F3XWzWkZF10OLNePPNXl8QyaM7uHd+0ATS6wNEzDd255e8PrOpwID5c1imhh86vf2u4ZIEqHcgfh4Pu6906lFERC9AJqikwNVDQTVJJBvtGMwFjN8wjPL1ythdyxl8Arz5auHHjOo40dyo0YYrMvVzeP7gZab0/5Uv53+U6fWE6QoiJjUt92H3Xcq6icvArSG4tcj0GhovDpo7SeOJcDchApMdBSYVvSRzNfa+1TZPX0+KVCjksAuzYlLYtmQPc4YvIOJ+VLqyXIu9z4gTi1ARVkqVIlRUBDMubWHb/TPcTgjnUuw9ppxfR9MtX1vJVqXJS9RtX8Om8iTrZIpXLkrzPi9btkmyB+7exSj6UjkKlc5vt4bHhN5TObL5pOW8UrOJhBCc3XeBzp+0pmS1YhjcXHBx1VOwdH6GzhvEN+u+tBSc+rrrJCKCI63v8yn/H3IrjBJVCqTIKdDpVSQpc8bYzu/eR+cisFXnSkKB5ONg/Nf8t0tpvAt8wamj7diwIDBDCgyYg51z5g/gztmV/DnBlcmfFGD2t3m4eSmt1UGng+Ezr+HmrloH+aa4XEpWLU6f75Yj5zmBnHMtkscb1l2sPXoheX8OuKdsSHmvJG8k3/FI7o679rp5F0WI9M5PkDb+RrVs9/RRWXnxFGuunWTmznO06hmKLD9ca0WicPmClKtbOp11nKNWq6pMPzIx3U7vqqKyY9HedLPgHCHiF+D41iMj4v7K9PzPI1kN6k19vYholhiNJ4K7Ln0TtyoEnvq047p81pbxPW0XjkvtxhMfEc+8b5ayduZmftw9lrxF7Zc9X3xjn1n5yIAnNUEx0mvPVLY0+QoXWc+KWwfZ0z+JBBc/XFdHIiWY59K56GjWsyH9v++VYbP+rYt32b1sv8Mxu5cf4I+LP9m1PJ0/dJlzBy7ZPd7gauLjCVtxdRdsWeJLWLAL/oHJRIbq+WdeTnT6lC7S6SJo1DYSvcNfEB0icR2Sa33Llk9+f4fPm47h0tGrZOQtUEwqxsRkepdfhKzLbVa8hMSBzT5M35zWOlXipQSmbznP8t8C2brMn/gYA3mK5KH1gGa07N/EbqNMSLHqeb4F7l0haStxkffYujiOs4dkZP0dqryyi/qdamFwdbF9vEcnSFrn3Ik5IPUtzl80iYHf3KZSvVi+GRAEEnj7ufLVok+y1eVSuFxBGnWty+a5Ox2OE0JwfPtpmrzZIHMLJZ/CcQC1OQPsRUJFQs1iTEtWj39e0ZQYjSdCAY8Ainjm4lrcfQfJq4JGudN2tH3ljfpcOX6Nxd+vttQRSVVeJOsJiAyJ4utuk5m6/1u7shwIu2TXreWIRDWZP6/sxF1vYMq5lJtU75wkvR6A7lISsgJycQ9aNW1riRmINSWSaDLio3fn1PazXD5+HVd3A7VaVbGUoE9l76pDyLKUtsP1Q9y9EsyNc7ftFjI7s+e8w2JorXuFUbhUArIOen5mHTj6SsdIVs/JwcVTRTF4eGFKVrh98a6dsvHg6p6eBqKCGmO1xSfAmx/2jGXn4r0smbSay8eupTOHmQIl81oUPLOLyPzOFyxuP/g1T8Fk3hl9h3dG30HynYTk3sqptVKRZC+O7CnCyA5LSIhNMFfflWDjnO389vlcxq0fRpHyhdIeaKgDro0haTsPKgOnItvY5phUo129llG06xuCwVXQ9pOfyFkw4+0A0sPZGkJZqjUkuZF+eejHl2mo8d9CU2I0ngiSJNG/RBOGHPvb5n4ZieZ5K5LfI8Dmsf0m9qROuxqMHvkL4Vtu23/mUODCwctcPHKFElVsB3dmJZR99a1DhBitb8y4ySjl3VEAWYKfz2/greKNmXlpCwfCLpkVrkSByz+RuC+MRIox8fP7Eq+8WZ8Pf+lnsQokxSchpd8KGmOC0e4+SZYc3htavBlmt2JshVpxVKiVCO61kX3H0LvUILvKkKpIhNxxIWfeZAdxS7K5lP8jGFxdaPJmA66euM61UzfNKb4OKF6lCLcv2m754LRbSsp4IOqti3cZ1nqcuXCcsL5xRwRHMbjJaOac/yFNt2lVEUQZx+Cq/oK7vBhIVbTkFOVmc4ZlAbNlpv+Iu2CojRyQ8cJzzlCqejFLU0lHlK2dNr3cWSTXxogUN6NtdODWNNPzP488rYq9/wVeTCeaxlOhcZ7yfF62LS6SDgkJvSSjSwmQbJynPF+Ub+/w+HJ1SnG3ieyU0fScgwJ41XIUtaybUSKT4zE5UDJUIdgXdpH++2eYs55Stgs3CWMbP2K+y4/iZW66uGXeLsa+PsVybNGXgtK9obu46snnoENwpZfLO0xRHd6jCJsX+ztQ5BRQbgDg6ev4xr/mjxw4NmgpSO6d7e41JStOleC/duqm3eq2x/d4ER+bznspeYJr7fQXgpRmmIkIobDyp39QkpVHFDlBhVqxfDrlKt/OP4B6vw5qcFXU+82Jvz+TmUP/onPut+ia913aFTjB0F4dOHl6DJL/DESOHeD7M7hUJEs/vT72rYxCCIQajXjEAuYsAXn8adi5tt3idzq9TJWmL1GgpOMqyQ5xb5eSom5LATVb2SSPXpmf/zlEi4nJPJolRuOJ0rFQTZrkqcD6O8e4lRCGt96dJnkrUNTL/o05FRWByUbhOFukVru1RedCtVhx86DTMj+Mp94Vo2pKN0hZtWUO0UmoeV1I6pED92nm7r57Vx/i/MFLlKpenJotqxCQ15/I4EibLiVZL9O4e/00T/4PE1S2ACWrFePC4cs2LTK3r7ny/UeFuHPNld6f28qg0YFstoa9/HpdLhy6bHet5TNzUq9lFEXLJjySTpziKvAcCLog7l27j8loIldQoFUcSakaxVF+SL+4nKMS+kkJMr+NzssHE+z3tpI8+yFJ7nb3A6hqLER/BYkbgWQAKlYN4ETpnFw5bT5WlgUffneT5q9HoKpmN4+57xQINQY3aQLFi/mSEFuIVCXlyObTHNl0moKl83HrwjQAytbMS8e3r1G3RfpB6GlxRdanrcYrhICERYi4WaBcNW/Tl0TyfAvc2mUodmbgz29x7fRNrp66kTK5+R9JlsgdFMjgObZ77VjLowKqzQJ2kuwF/n8gIv4HaggPFDoBGJD8fkCyUUhQQ8MWL6bqppGtCCE4GXmDaRc2MOnsWtbcOkyiYt/l4WvwoGvhOnxSpjX9SjRxSoEB0EkyuV/Kg3BL5wdZgspNKtjdXcw7D8PKd0BCyrBFpl5g6UzF01jQSRib+CBczeeg0+vYMm+X5f+/+PsDdAY9st5aLlknk7dIbvqOf9Pu1HevBPNutc/Nioc9S0uKyXn+D7m5etZWPygFya01AH65fB3e/JISdHzWsRirZuckIe6BvKpUAOE9ls3LXuJ/ZT6gR9H36FP6A7rkeZuZQ/6y1Kmp16Em7hnoSWULSRKsn5+Dv77Phaqag5KTjeZ/VRXCozuCp+P6QaoSCSENIXEtqQoMQI1Xwvlx7UWqNDBbNTq9E0KzLhHAgziV1MuT+m+916IYMOrOg8lT3oeb5+5Yqt6ePRDM6LcK88eEPBk8Wx24NUEkn0CNGoYa9gZqxLuoCWsRUV8gor8C5dqD4aaLiKjPETH2LTe28Anw5se93zDwx7coWiEI7wAvCpUpQN/xPZh2aDw58tqv7SOMh1AjBiCCy5kL2IW8ioj/O6Uq7wMkl1JIgVuQfMeD22vg1hzJ+3OkXLuQ3BpnSN7/AiqSpX9Spl8vaGCvVuxOI0tEGuMZfHQuxyKuo5PMrh6TUPHSuzKm4uvUDSyVrevNu7qbX7+Yi2FROLYyg4UM1VtXYdzyoenOdTHmLkuu72N/mDmbp5J/YbbeO0miavvJ/yW/Qoyv/Aatt4+3b4lJjThOB6++19DdTkbWybz8el2GzB1k2Xf15HX+HreMXUv2oZhUvPw8admvCV0Gt8UnwNvmfNFhMfSr+AmR96Ocajmg0wla9gzjvbEPWzB04FIeKWABJ3ed59PGI81P+E78Qri6q+QpaCTZJHPnigvuXu4kxCamid+UdTKlahTnuy0jMLgZWDV9Az+9NzP9BRwgyQIPT5Vpm89xYo83CXEy4cEubF+ZgzJ1G/LFvA8dHq+GdgHTMdv7FIiL0fFm9TL8sfcsfjmdsBwlQ/fK5Yhy2F3bzPfLL1G+poPCehZSPlSuTSFpIw8ubHoBsilHB8xDMlR3Yp3MIxJWIKI+x/xsnHqdUuVuguT3I5L0GPoWPEaeVLG7zlt64uKZtWDm5Dgji1/584W7v2nuJI1MowqVDw/P4Xy0+cnzYQtFnCmJT4/MZVatdyjjm31ZFJ2DarF94Cku3t2Py85YhAySiuXfXJXyMuyPD5yaq4R3XoY+EofzYekWfHL4L05G3bBs00kyLfNVZnC5thhkPW8Va8yvl2wEZ6riwX0lnbL3UuKDa5WncC6rfUUqBPHl3x9h+sNEUnwS7t7uaerK3L50l5jwWHIVyklAHn9W/7KR8HuRDvoFWaMoEjcuuGK+4aT4RFzrI/l+hyTp+HOkuRCgs/MlJchcv/DAqpIQmxLD8sjhqqJybv9FVk/fSMePWtF6QDMWf7+Ke1fvp3Mvtq8dClUiLkbm6E4fmnUNZ3DnYpza7wVAfNwJh3KraoxdBQbMWVjefgpt+4Q6pcCAuehf1UYxbF3muBqxi0GwaZF/OkpM6jm7gKFWigIDDy6WM++PDhE//7EqMUK5j4j6grT1b1LkS9psrk7skbGWCC8KWhfrzKMpMRqZ5mDYZc5E3bK5L/Wn9c8rOxhXuXu2rWmQ9fxc6y3mzCjGkjVbSF53H/leMl65vGjVqwlvv9HOYTxMevgZvPi99gAijXGci76DjERZvwJ46R/coN/IV4f5o5YS3d4bPB4oF/LdZFwXhpPwsQM3gSKQryQhh5l/6FVVtSqK9zB6Fz16X+uv6MENx/h96DxLarIkSdR4rTLXTt10WuEAcx8lz5xVkLw7Ay7gWg9Jb87migqN5vj2x1enQwjBqmnr6fhRKyRJYtj8j/jk5ZGYjMkOrEiOf6B1ejh/zIPm3cLpP/IO77cwx1QINR2rlNFxXR4wu6cKFs9YlWODm/11PbwUOr97n1Y9w/AJcKAY6QqDSzkkl/IIfWWIyKwCoEDy2Uwe6yQJS0gvdVzE/YmkKTEa2YymxGhkmq33TqGTZLsxIopQ2Xb/NCZVQS9nnxnZVedC/5JNeevDxoS/G4tB1uNnsB/smhn8DJ7UylnC5r5dS/cjzbuPz5IQTJU9EJ4y8t1kdGcSkYDkOl6YqnuC7pEbrxCgk3CbF27Z1H1oB4eF+R5m55K9fN11stX9XAjBwfXHUNO7WT+Cqgoadm2J5Fk3zb64qHgbR2QjAu5cDqZbwf60GtCM9oNe4+f945gy4FdO/3s+7WDAGR+dTi+QZShZMYFeg+/y16T8VEhpGmmf9E34kgxhwS4kxMq4ezl3na+esR1I7OGl8N3ySxQunWiz0rF5QQ/w+B+S13sW94uIyHyZfwDUEIQajSQ/HjeDSD6LY6uQAOUSQijPnUvpSZAd2UVadpKGRgaJV4zpdpxVhSA5m5WYVPSyjlxuvtk+b3pcO3UTvYsOU5KCy760rgCPCfeIH5rXrMiYUlxMKX3u3KcF43LAfEyRCoXoPSbtk6kxKZldS/axdf5uosNiKFAyL817NWJK/19txqhkuPCYBAVK5qNehxo2d/vn8cPFVSY5KQsBzE4QejucP0YsZNuCf2nYpbYdBUYiX+FEgm8aUFUJYcdkrpgkqjZ8kFbc/cP7nD/mQYcPWjoWwlCT9ArQ6fWwe60vLi6Cdn1D7CsfmAOKr5134/wx20rMGx8HU7iUAwUGF/D/E9nwkvXmJMdVdNNFxCDCe0KORdYtFrILyYB1LIwtdGi5JLbR3EmZR1NiNDJNYU/H3Z4Bcrp646azXZ79ecXNw9VhVV0pUeA54g6mEq4k1/MCDxn5djIuW6KRYx7cLK+duknk/Sj8c/tZtkUER/LZK6O4fuaWpfLuxcOX0y0FnyEEvP55O1wMtt8XN2kFTTqFsGFBgM0GitmJUAU3zt7izxGLbOw1r33nmht9htxh9re2a5PIOkHuAkZqNom2bFMUGDhekLtiOc4duMiaXzZy9eQN3L3dadi5Nk16NMDdyx1ZdkV1bQpJG2zOrZjg5D5PLp7w4PoFN0pWiqd8zTibBf5UBRLiZMa/F4Qty5GLQaXFG2F2u1ubSYbwTqiGWkg+w5H0qZ2s7Wf7OYcA0xlzBpa743pMmUFyfRmRuNrBCB24vvxMdKbW+G+hqcUamaZVgaoO98tIdCpU6z/3w1WnXXXH1g8JAvL44nrVhPvsMNynhuC6ItJKgQGzK+j+jVCrbaM7f8/NC+ZA6dQYF2eyjSxLpxNQnMqkfr8w7cPZxEVZW5KEMCFif6LnZ/fIkTsZne4RZS2TzSIdkV4sj04nuHXFjV6D71r+BlIaVwpy5E5m7N9XrJQDnQ4Cc19nzc+jeL/WUDb/tZMLh69wYsdpfhw4k/+V/Yjbl8zzSX6TQFc8RRazAmRKybS+cNyDr/sVBsCYKDOka1F++KwAt696IR4x38uupVg4s6tVgPPDBOQy4ent5HtpPIgI64owmWu+oLft2swYMiJ+aTbMYwO3ZqDLj+0CdgAqkufbj2ft/wCpvZOy+noR0SwxzzAmVWF3yHn2hV5AESrlfAvSLO9LeNhokvg0yO3my0elW/L9uTVISIiH/BwyEqV88tEtKG3MxZPkWux9Ft/Yx877Z1GESkW/IDoH1aZKQBGHx929GszaXzdx/tBlXFxdqPlaFZr0aICnjwfFKhameovKHN543LYyIyBf8bxEBD/qHkmLp585lkcxKZzYeYZTu88BAm9/BVmCqHAdTuVsp/BK93ps/mtXuuNUk8rKqes5tu0UU3Z/jYd3ivsj+RSoIQTkgh/WXmTWN3nZvsIPU7L5hl2kTCKvvx/M2rk5OLnX2/yOp6PXSJKUrtvREYoicfqAJ59OuYmbp8ofE/KQN3cyeYKM1G8ZRaN2kbh52FYOXus0jwBfb8b2L4xiki2ViiPuRfBly3H8fmYyOp0L5FyNSFgEMb9x7kAU1867sHuNH0d2evHw9Tcly/zzdw7Wz88JwIiln1CnTQWQ3JAkmbcnCAKD1rNo4kpCbpo7PXv4uFOmVkly5BGAswG2Coh4RMz3SP4/I3m8iYgekskrmIoKqq0Ch9YIkQRqBMi+6RYJTEWSDCkF7HqDcguzMqOSkqqH5PstkqFKFmT/b6O5kzKPVifmGeVmXBiDDs3mdkK4pSCbIlQ89a5MqPwm1XMUe6LyRBnj2XzvJGFJMQS4etEkz0v4Gcxl6bcFn2b25W2cS0m19tK70aFgDf5X7OWnqnBtDz7D0JReTanBx6mByP1LNOGtYraLav3z+xam9P8VJMmspKT8Nvjm8ObbjV9RvFIR4qLjGdZqXIrSkXEkWaJohUJ8u/ErFoxbzrrft5hL60uCKvVjePuruxQrl8idawaW/xbIqtk5eFSZyVckiaoNY9C7CC4e9yAmtiS/nZxMj2LvEXwtxCk5ZJ1M9y860GtUVwBE0r+IiD5WY+KiZYJvGfDwUsld0IgkSUQlDWLJNF9Wfb+cRNW+Qbfd+3W5eeYEh7dEAxIGoWDMRGBn7gJJ9Bt5lzF9zb2YWvUIY+A3t3GmVqGiwMaFAUz5tGCafV+vHkLNllUfGa+w5pdNLPthLXcuOb7p+wb6sPDODHSPBLkoisLdy8EoJoW8xfJYKhWroZ3AdArnm0DKkPMfJF1BRORASNrqcLQQOO5l5VIdOcdc28eabiFip0LiaszuKx24NkfyHviQW8sxQhghcRMiaRsII5JLGXDvhKRL3/X8LPKk6sS03PB2ttSJWdt85nNxf8tONCXmGSTBZKTL7smEJsWkyfyRkHCRdcyr8z5BXun/MCSrJv65c4ylN/ZzMz4Mb70br+arROdCtcjplv41EELw59WdzLi4GZNQLEqATpLpV7wJvYo2tLiLQpNiMComAt28cZGfrpEvJDGadjsmkizsBxr+VK0PNR/JQDq56ywfNxpu07og62S8A7yYe2Uq7p5u/PDODNbN3JKpwFqAIXMHMeerBdy/EWo1h6wT6HSCb+ZfscRfbFnqx8RBhQAJD2+Fz364QZ1Xo1HVlKQnHcTHF8Cz0Az+Hn+cP0YsdDrl2jfQh8X3ZpotJsodRMjLpF4AY6LEzjV+nNrviSTBS7VjqdEkmlOnhnFr6CLqnN/GZzQgxEaDxaqNYvh67nXuXjfwv3olCRTxTGQnCynJP5Lt5py2EfgHJqPTQ+hdF0DC3VPh76NncHNXcSZmXDHBG1XLEhHyIA5I56Kj7buv8s7k3jaPuXLiGv0rfZbu3G0HvsrAH99y7kwStyEi+zs19gESuDYH70+RkjYhYueACH4wp5BQFIFen54SA5Lv90jurdPKZbqKCOsKIgbr4FwdYEDKMQ/JpXwG5X7+eVJKTIv1fbNFifnn1d+e+ftbdqPFxDyDbLh7nODEKJupywKBIlTmX/8XIQQnIm6w5MY+Vt46RHCidS+WJCWZDw7N4etTyzgXfYdYUyJ3EyP548oOuv37I5djgtPM/yiLbuxl6oUNJAsFgbkab+q/0y5uZMH1PZaxOV29yefh/9QVGIAVtw46bA+gQ2L+tbSddBd/v8pu8ztVUYkKiWbb37sxJhrZ+MeOjCswgKePB1/+/SGHNhxLo8CY15EwmSTGvRuEUM03pSadIqndPBqDm8zYeVcsQayyjCXTxcPjLiKsO20GVCKwQI40rQvsERUSbSlOJ+nygaE+oOPcUXferFaWiYMKsXFhAOvnB/Dte0F0KFWerzsso875beQnlu/YQaCwTsvOmdfIqNlXkSSF/EUTeLXhPb5jB/mJpSsXMDhQLtMiERFiIPSugVQNMCFOx5i3C2MySZgeKrBs75FMp8cqeykVR+/f/RthTkm38uf1nNzlnJtIcnsZyWc0Zk++s+Z/AUmbILwbuLVEyrUDKXAvBMwHz74c/bcIS37JTf9XSnDhuDuKjYLTAhlcKoFbc9srRI+wocCQ8ncSInJwllyCGo7JcsuBbHBHPa9oSswzyPbg00gOfuAUobLp7gne+PdH3t7/CxPPrGLsqWW03T6BkScWW/oW/XZpC0fCU5rBPWRaUBHEmhL57OhfqA5u9EbVxG8XtziUdealLSQpyQ7HPA2Ohl+13YQxBQXBkbCr7Fyylz2rDloCXA9tOIbqIJBWkiQObjhGVGgMxsSMZ4w07FybhXdmUKXpS2yb/6/dm6hQJcKDXTi41fxEpZigbZ9Q6rRIpGy1eDsZLgqIGDwNS5m8awzl65R2SiadXsbV/cFToOQznLBgf4Z2LUZMpC5lfemhTCUJo6TjMxpwB0/yEZdGkXntzTBknTC7e24n89Hl/eQjjjt48rmuPkZJtgToVqgdg3OVZ605stObd5uWZMPfOYgK15EQJxMd8YhbxwR3rxu4d9MFvYv1GkqyQtk69tti+AY69zQryxJLJjnKzLFG8ngdKXBXuj2drFFADUfE/IAkyUi6HMiGqkTF92VoF29mf5OHa2c9GNK1GNtW+FkpMqZkuHm1GpL/LJvp1cJ0HYz7sJ8erYJyCZKPZUBeDY0nw9N/ZNZIQ4JitFI6bBFjSiQ+zlxFNHWkimD9nWOEG2OZUOkNlt7Yb/dGrgiVW/FhHAy7nMalksrhsCtEmxLSleNQ+JVs75GUVZzJiEqMS2RMl0kAuLi50Oad5ulaVoQQmIwmPH09LCnQGeHO5Xu4urty9eQNFJNja4ROJ7h23o1azaLR6SGodCK1mtxFMeEgTVeB+GXkKjiY77ePYsOcbXz3v2n219DL1OtQ06rKsaQvxJrFfUhM2IijGnohkgefioZ8xw6LIvOpaEiI5EHFurFmGW8nI3W8jXQzGRHkQshnhXlpVxLR4SbyFDLSvFs4Ny+5cXKv7Z5Q6XHzkhs/DinAj0MKADBhySUq1onDlAyLp+dixcycRIaaXUh6l4dddjI+Obzt1soBKFW9GLkLB6YbX6SqgsMbj2dIbkmXA8n7I1SRBPGz0uxPNXpYf4wVSFyNUL9Cks3uu8j71tbX+BgdEwcFMXNMPkpXjQcBF45706zPa/yvtpdtYUz2O5Vbj7sEhsrOjdXIEFpgb+Z5riwxO3fupHXr1uTLlw9JklixYsXTFumxUMI7r1PdlRUb5l0Vwb7Qi2y8d5w4xXGpdJ0kczLS3CMowWRky72TLL95gP2hF1GESkw6CkwqMcnOjXOWewmRTDyziiabx1Bv41e8unUs40+vyNA6NXIUd2jNwiTQH31gOUhOTGbZlLV4+Hg4TFOWZInSNUrg4e1OzZZV7Lqe7JGUYLbeuHqkH/CsCnNjxQfHyvjmMKVTZwQQD+qlNOvViAr1y9iUU5IkJEni9c/T1g3ZueQUqhMenxDJg09pmMYiExctP1BgrpsVGLE0PxU6Gvl0yk1G/3mNd7++Q7FyidRqGo2bp/3FJEng428ivYqwOfMaKV8zDkWB0W8X5o/xeSwKDGDJsAJw9TAweuXndmvlAMiyTL8JPdK/CICiJKMGV0O9Vwr1fmNE7G8INf3GjpL34BSLjNlCkqo0htxxYWz/Qiz8Kdcj7iEjqA/S8v3z+Nn0SkWEuLB3vS97N/gSHqwjZ/4cDoRwLgMJG3FPGtmDIOtp1i+qs++5UmLi4uKoWLEiU6dOfdqiPFbaF6zhMJ4jPXSSzL8hF9IdJxDIyPx1dRevbvuGocfmM+70Ct4/NJu22ycQnuRMd10o6OHgBzKDHI+4Tqddk1h8Yx/RpgSMqkK4MY6lNw/QfOtYTkXcSH8SoE2Bqrjq9PYVGb2E6/JIq01CCGLCY+1bVyTQ6WRavG3OauoxvDOyTna6NouskyhZzZxVFlS2AHmK5HI4Xgio1dSskCgm2LHSj7vXDZYaJnaR3BDGYwghkCSJ0Ss/p1Jjc1CmpJOQU+QVQiDrZFb8/A/Xz9y0miIxPtGpcwLbisyCHr6E1wu2UmDIb6e4nodKr8H2soAEQpDiJnJ0nSVefz8YhPk67d/ka6nu6x+YTO6CSVaWmM9mvYeqqHz/9nQ+azKKsd0ms2/NYRTFWplq0Kk29TvWcnj+sk5QtmpMivIoQL2FiP0eEd4NoZrjcITpJsJ4FGGy7jUmSTKS10fM/G4AE94vyLRh+RjarSi9apZh1xo/Zn+bh6/7FcZKLPmB1cov0JcaLSo7VKZ1Ljoada1j/wQMVUFKr/K1AYErasQHqKEtUcNeR8T9hVBj0zlOwxm0mJjM89xmJ0mSxPLly2nXrp3TxzyL2UmxyYmsvX2EXSFnSVJMlPEtQMeCNdgWfJppFzciI1lcQhLORw7UDyzNmahbhBkd/8h0KliTJTfTNsGTMGdC5XHz415ipE23lIxEkGcgC+p9kOGCdklKMtfiQpCAwl65MMh6EkxGXts+jjiTfQuSq6xndaPPneqVdDDsMh8f/pNk1fRAfkWADG7TQ3BdE5XmGFknkb9EXm6eu4Osky3uJZ3eXGPki78/pGHn2pbxx7ad4tsePxJ2J8Kp8/5hz1jK1jI3J3Tk6pFlQYM2kQyddgNFgaR4mb6NSpEjt4kf111MZ5WUT4prcyS/7y1xEGcPXOSbblPMHaMfQqeXkXUyX6/5giqvVADgy9bjOLT+WIYClwNFvMW1lModPLn5XVGqv2FWihQFmyX3hYDlM3Pyx4Q8JMbpkCSBEBI6vYpiSu9Zy9yewDsgmfZvhbJ/sw8Xj3tQs2k03T8MpmRFswUvNlpm3V85+HtyblzcfIkOi0Wnl1FMquW9Ll+vNF+vGYqnzwOrQ9jdCN4s/A6mZPvWouG/X6Vui+hHturA8DKIMEg++mCzSzUk78+RDBWBlIy4hsMdnuHQ6ddp1DYGDLWQA2Zb7bt68jrv1/6S5KRkm+/XW+Pe4PXP2zmcX8T9jogZb2evBLoSoFzA0j8jVaGU8yAFzEXSF3I4//PKk8pOarx2AHrPrJWjMMUlsbXlL8/U/e1J8FxZYjJKUlIS0dHRVq9niQvRd2m/8zsmnVvDgbDLHI+8zuIbe+myezIeelfGV+pOGd/8lvF53f35pHQr/F0c38B1kkxed3/eKFLf4ZiyPvlZdfuwzf0i5WXQ6dHLOuRHnoJlJHSyzJfl22dIgUlWTfxycRMtto2jx56feXPPz7TY+g0zLm7mnztHHSowAEmqidUpMkeHx7B6+gZmffk3K376h4hH4gOq5yjG0gYf879iL1PaJz/FvHJjWB+F17s3bCowAKoiyFMkN18t+phydUrh6m7Aw8edRq/XZeqBb60UGIBKL5dn3vXpjF75OaVrOq6q+sawjhYFBqB575d565vuSLKErJPR6WV0erOyVbNpDB99Z64oGxOhZ0jXYoTeNXD+mDv/zAuwm4VjJmVn0iZE9DeWrUc2niD4etr4DsWkYkpWGN35OxLjzde/7buvZjjzKkTyYDzVrbaNl6rzzajiJCVICAFTPstPYrz586IocHyPJztW+XLmkAft3w5l4fEzDJl6jcKlU5Qe06OfLUGRMvFUaRBDvsJJgMDTRyGoZAKFSyXhm8PEpRMetOwZxsjZ1yhW/oEL0stHpWO/ECYuvYwxIcpy7vAgS+nM3gtpFMscef35Yv5HKe/PQ13LUxp8dux/nzqv2vptUcC4GZIfiZdJPoII744wmj/Ha2dsspr3UWRZsPoPc50gyWtQmv1FKgQxaccoilUqbLXdN6c37//8Nl0Ht7U7twWP/4Fnf1KL05nDJVO0TX2lFAUm5ZwAyy+Eeh8RMUDLXMoimiUm8/ynA3vHjRvHqFGjnrYYNklUjLx/aBYxyQlWNo5UN9J3Z1czqWpPZtd+lzhTEopQ8da7IUkSkclxzL683WHQbpsC1SjunZtrsfdZdfuwpb5LamXdAh4BNMtbkTPn19mVUSC4FhdiDhK+uZ/9YZcs+6rlKMa7JZtR1reA0+esCJXPj/7NvyHnrQKXY0yJ/H55G7mcqFsDsO3uKVyXRDD7qwUoyQo6vYwxh8ykIyvJ26QwJcsXoVHusjTMVZZcbr70K9GEfiWaANCp41tEhdjPKtLpZXLmC6BBp9pUbfoSm//axbVTN3D1cCUhNtHiorE6Rqejdutq1G5djcvHrzFzyDzO7r9AXKQ55qZ0rRJ0+bQt9TvUtBwjhOB2QjiV3qnBL69XZ+9f+7l7JRjvAA8atXehRIXLgEpiUll61V5CYtyDbs4/DC7A3esGOg0IwSfAUeCKCgmLEN6DUIUvK6f+Y9dVJlRBXGQ8OxbtoXnvl6n+aiVe66ln3Z+psSjp/0AGing+56DVts/FQT6NaciK33OSI08yGxfkpGjZJHz8Tfz+dT7Cgh+4mPIGJfHe2NtcPevOtXOppfsfUZ515vL938w3Z92d2u/B9K/yc/2iG3qXRCQZipaL553Rt4G0Vh+dHoqWS6Bj/xDmTc6T9oopKruX7+fu1WDyFnnQXbx+h5pMPzyB5T+uY+/qw5iSTZSu6krbXkeo3jjKYW2WtIXtzH+L6OGQYw3XTt902FpCVSVuXHBH8v8FyU5gbcmqxZh2cDxXTlzn9qV7ePp6UKF+aYcxPw8jSRKS9ycIj26QsBKh3AU5B7i1hojeDo5UzJlLxr3g6sBlpeEQLbA38/yn3UlJSUkkJT14so+OjqZgwYLPhLlt1a1DfH1qmcMxqUXtCntZx05EJyfQe89U7iZG2oyd6VCwBkPKtQPMN8tjEddYcesg12ND8TG40zxvRZrkqcCC63v45eKmdONvfqvZn4r+QYQmRhNmjCXA4EWgkwrHw2y9d4ohKRV0s0KBbRAz8YFLJamVL4kDAs33WtnsBhMSBHnm5Ofqb5H7oU7XM4f8xeLvVzu0MkzeOZqwu5FM6P0zyYnJ5norwtwaoGztkoxZNQSfHOln0yQbk5EkCb2L9bPCjuAz/HpxE5dizXV6dJLMK3nK837JV8nt7pdWngG/sn7mljRNJ6s3jubrv66mK8f9qNFsWerCnGELHI7Tueho2bcJ7/9s7nGjhA3gnznHmTMuD1Hhjp93HnYl3cGT8VTncw5a/v6UhpaCeC4GlWSjzKPKkSSZVVsXF5Gy3z6/7ThHoRJJKCZINkp81KYEV8+5IVR4e9gdOvQPddhtOvy+nm6VymJTOZNg0OTitBrQwez6saOhiLhZiJgJOF9918ZSOZbxabPFnNx5xqF1LW/RXPx56cnHAgrTTUToK+mM0oPn28jeHz8RmZ4kT8qd1GD1u9niTtrZetozcX97kvyn3Umurq74+PhYvZ4V9odeTOOieZRkVWHgwVlp6rD4uLjzW63+NMpd1moOb70b75RoxuCybSzbJEmickARRr3UhTl13uXHan1omb8KrjoXcrv5OhVAnGohyenmQymffJlSYACW3dyf5pylCBMu66MwLI9Ad8WxKwkAAYkzHwT3JtfwJPHdXCBLoJNAMiswALfiw/ng0GyrWjgdP2qFf25fm+Z7SZao264GQgjGdpuMMdGIEAIlWbGkQ587cIlhrb91ynzuYnBJo8CsvnWYz47+xeXYB3EpilDZcu8UvfdOS1OwEODdyb2p+LI5MDc1gNPsfnIsw+HtXrzfogQ9yixNV4EBQICLwSzvprsn+O2WSovuYSw4cZoBo2+RM+8DC5ZOp/JSnVhGz/dPo8B8SkPOSDltZi0BNhUYMFeeRZCuAiNJgmO7zenCOj24GAT9RtxBqOZILv9AU7rBYwG5THh42f7sSwhMsTsR4W8gwtqkCca14NqArCgwACg3adSljkNxZZ1M4272XcOPF2efcbN4HV5wNHdS5vlPu5OeZRShplsLBuB+UjRb7p3itfzWZuQcrt6Mq9Sd0KQYrsQE4yLrKOdXEEMGquU2yl0Wd52BBMW2e0VGopJ/YfK6+zs9pyNuxIU9cIGZBG4zQjCsi3rQJ04vEfNHYYSvzm7tdPl6EskRD5SdpM7+5mBdXdrxilC5Enuf/aGXqB1ojkXxz+3HD/+OZWKfqRzfftoyVm/Q07JvE/p915MR7SaYS/DbcL2oisrZfRc4seMMFRuVy9D5x5oSmXBmFUCa914RKpHJ8fxyYSMjXupstc/V3ZVx67/kwLqjbJi9jZCboeTIH0C1JjkRYrzNS7VrjS9j+wdlpHckikmhRsuqzLu6mx/Or8NN8qO9vxvuyUbc3AVFyiYQkDuZ/EWSaN83jJKVkpDjP6W4oSOBxrQWF0d1ZOwL5pzAZoXFjE4PlevHEpjfSMhtAwlxunRihszxOMYkOxYWIVGqUkpwsukSIvxNyLkGSbausyLpiyP05cB02sYsTiL58sqblZn/7XLC70akcSvJOhkPb3davdPMsu3O5Xus+WUjJ3adRaeTqdasEi36vkLOfAGZl8Meunxmt5LqqHqxCcnFcUd7DccIIVmy6bIyx4vIc2WJiY2N5dixYxw7dgyAq1evcuzYMW7ccC7t9lminF9Bp55xZCT+DbHfZDCnqzc1chanckCRDCkwAG46Ax+Vbml3Xb2s44PSr2VoTkf4urhbblHuP93HsDYKKaW/oiRAShZ4fnUbKUa1Wz++9sEH6dzCXUIp525TgUlFJ8nsfuT65Q4K5LutI/n9zBSG/jWIrxZ9zMLbMxj401sIVeXwRsdZOTq9jl1L9zl93qlsunsCo2o/P1oRKhvvnrAZ3JwadzNy2WdM2jkaBPw0aD2HtvtYld0Hc7+jyZ8WQAjrm70jZJ1MkQqFyFenID+d/weARKHnnW11eKteGaZ8VoBDW324cMyTHav8GdSyOEPr5yO8fGcCjVFpFJhU7NWRyQpCSJStnjb9PzCf+druWuuL/uFQkATr99Jkgr0bfKzqxjy4DoLSZWIoVTlVRgXUu5CwPK0ciRvSUWDS+T7KAWCojoe3O99vG0X+EnkBs1tP52L2hQXk9Wfi1hEWBWXLvF30Kf0BS6es5fyBS5zZe4G/xiyhV4n3ObwpYwX3/s/eWcfHUXVv/HtndjfuabySujsVqrSlFC/FoRQv7i3u7l4oxSlWKBTqQI26u1saa9yTTbK7M/f3xyabbLOWtMj7I8/7yUt35M6dWZlnznnOc3yBEAZE4CTck0sV1MSaqFQzmvH343+KxGzevJk+ffrQp489KnH//ffTp08fnnzSc3nivxHnJ/bD6EM3Xx2JxRfXsSZifMvTeLbnZU66EYBOoQnMGDjZqTrqZHF2TTRJybBg+qMU4YKnqEcshN6WRuIqQYBqLw0WQI+wljwkx7Dni+2ObaXBtxu0RXfRTAZo1TmRUVcNY/glgx0al+pKi9eneJBUVfiQ+joB6eYCryaGVqmR5yKlVB/v3v4J6xfYK1vemZpIUa7ByRBtzaIwKkob05vHHmEqzivl7de+qiOQVknJgyUU5dnHqn3S0zWBSde55/BGoiqLOE4Qi695gpKAsBoNkOrkW3IikXmNlY3snVQHRZV06lNBh54NjQ9LCuykYceaYPZuDrQbx31dghidBpl2gqPXkOblc8I5MVWiqJI2IWW8Wboc8Y3zeyCrnNsKSGlDlnoqGhAgwj2eiwi+DyHsbCu+bSwf73qTl397nEvuO4+L7zmXp3+eytdHp9G+dzIAR3Yc45Vr30PXdCeSres61moLT174AnlH5iCl6897kxF0M/jV6mLqf34VECGI8I8QTehM3ow6nKzRXe3ffxH/U+mkkSNH/r8p5Qs3BfFSn6uYsnWmx+0UBJ3DEv7SuYxL6M2Z8T3ZU5xOibWShIAI2oXEet+xkbggsT/fHVtDycoUpALCXbCjyEbFK4dZ+NDnBIXZy8ltusbt5z6BVHBUeYpyHZFvQ0a5Tz9pUqdzqO9ELDg8iLAWoZTkuS/H13VJ666+V2XVItQQgO7D5zfY6N5BNS+jgN+/WuG4/+YdN3HHWR255NY8zr6qkJAIjdSD/qgGXDYC9ISi7GK2PL0K0xWRVE2Kwri2HCXP9SAWoTJLduRyDvKgGEHZL7v58tD7rPllI5mHsggKC6SsqIJf31+IogryNHtqqbaLtaXRNz17H6bIGCuPTk91WqNrcHSvP5lHa4WRgpfu6MLnS3ajflCESLHChEz0nxKRcUaEgL7Dylm9IBywtyMIjdAYf04Wl/2xDTXDivygCHlxCATU6Hf0Ez4PlnVOzrmu5ovMh8Brwfw9UE2dx4o/ImQKIvBypz0URaHvmK70PSMChAmUWCdR8Zx3F9rTnC5iuFIHm0VnwQdvMunhDyDic4Sh8Z9RVxDCAOHvQdVvSPO3oB21k5eA8yDgSoQafUqO819Gc3VS0/E/RWL+v2F4TBcmtx/NjMPumywqQnBhYv+/fC6qUOgZ0fovPUaw0Z+PBkzm1mlPY1bwqAWUUlJebCYoLAibrvHg1q85uvQQot4DvJDgN7eYquui3BWZ4K+aGJfQ2+c5KorCBbedxTfPz25QDVQLVVUYe91In8esxZj4Hnxw6Hf3x0bQM6I10X7uK582LtzaQGtZUmDk0xcS+PSFBAxGHZtV1Nz8mkb4/b4vxHJmCIYtFR7J5iLRlqWyNRZUKK9i0+JtXHjHOACkZSu2ko+45cFdKKrEUi2oKFHZ+nsXtB394VvP/YjqYBcAh0ZoXHJbLmdfXUhoRN2HQK/RU336ojPRz8vUmNC7A+9/mUTrKUsRqVaUSzIp+6Qls+cm8uMHMdR+aJ7/+iidEsoxXp6BmmlFb2WEHxNrCAyACoZk52lp7hyGT4CxGyJmLVT9AXouqDHgN7aBvkbKamT5dDB/A7K45rBtIfhWRMB4Vv20niVfr/SY5tR1weYVIUyamoIsug6iF7ps+NgUCKFCwDmIgFOXXm5GM04FmknMP4wb2p3BsfI8fs/e6eTOqwoFKSVP9riE6CZWA/2T0KTO+vxD7ClORxUKA6M70C0siYTACK4ZPIaP58z0KGw2mAyOLsKzUteyJnc/IS6CAqZfirD2DkDrXaPFqLHUr03bPN/rCoIMjStdvHTqBWxYuJXD21KcbhqKKtB1yb0f3UJYtO/vScquVBZ9uozjR7JpfXYAacmVuHtomtzecznr/I/+8Li+VudxMhFLqYDx91L3TY3rwSJUHp1+jMyjAeSn7QFGIyvnopdMBU06+jz5+Uv8/G2MurqYMdcsocfpg/j0WVODBoYNYb9QpUUq5jIVk3+NOZ0OimLvJ/XKna3Y+qcz8ZO6pLpS5ebLgnnr69m0u/8mAlLzMJ+ZwzI6o9dEOKQi+PTbnjy2bD6JpVWkh0Zx5xm3MHhHFnfHrMffaAM0ROCVABRmF7H0m9Xkp+0nIrQFZ1xU7NDiuETVYkTAeAic4HYTKS3IwhvBuhknZq+lIEse5LtX1vD5s8e8XCc77J3GNdDS7MQpwLXmrRn/LjQLe5uO/1mfmKbg39h2AECXOgsytzErdS2Hy7JRFYVhLTpzdfIweoT/79l5Hyw9ztStX5NVVewgE5rU6RHWklf6TkQt0bkyabJbgy/FoHDmxOFM+ewOpJRc8Oer5FSVEHxHKsoxSwMtjTSAZVwYlgvC0ZNMGBWVUbHdmZg8jE6hTUvFVVZU8f1Lc5g3/XfKCu2tG3oM78JVj15M/7G9fBpDSsnHD87kxzfmOezthZ9Cxa3RWMeGoQiBIgQ2qRNs8OeJHhdzRqz7iqeU3WlM7vlAk86nURBgHRGCraMf/h/nu9Qugb3UObFtNR//eQCp20vUCbkfpeIdpLR5MYCD3LI3WLNA8Okj32Kt9tYQyo7AYI2BZ5YSEmHjtDNK2fxnCL9+4r4HlaIK+o/tzbABifR8+g6nMvBcEUjA8Hg+2PYtLUsLSA+NYvIFt5MTHIEidHok5TD92rkYg86F0Nf45vmf+Pq52VDTc0rXbEjg4sl53Ph4FopLuZMREbOhQeSlPmTFTGTZ87iKnKUd8uPmEZ19ujaKKhl/Yz63PH0ce2+Nc1DC3/Rp32a4xt/lE9P/53tPiU/M5glv/+vub381miMx/wIoQuH8pH6cn9TPpSPs/xJyKou5deMnmGs6aNf3odlbmskdGz9l5pA7ufHFq5nxYEM9kKIqhEQEM+kZu16gxGp2eKdUXxhOwNu5DfYRNvCbX0Lg4jK+SvmAFgmRJ30NA4L8uf75K5n09GWU5JfiF2By6HN8xdwPfuPHN+yC0FrCJqt1At/JRc4qJvb6Doy8fhitAqMZGdsVP9Wzu+pvny1r2sk0EooCsdHhpIw24v9lAbJaupQMSgkTJufZb94KgISKN5AIrwTGZoOUDc8y/f42jZqbuVxl+ZwIQNK6QzXLZnsuK9Y1ycZF28jNKOArnMu9n48ZzSM1BCYjJJLbBk2ioFwFPw3dqLIjPY7FB+/igpG3MufdRXz19A914+p1/YNmT29BQJDOxAdyXMzACtpxUDq6WGeHNLs3gFwwMwpFlTURFk+wN8o8b1KtTkcHzdV8mvFvRHMkpun4n6pO+i/gnyQwxRYznx5ZxsUr32D0kmeZuOY9fkrb0MBszxNmpa6jUrO4FLBqUielIpdl2bu5dMoFPPDp7bRoWVcyLYRgwDl9eG/9i8S0tIsFDfUEoNYxoViHBdu7ttS7TFKxv75h+rXEJEad0muoGlQi4yIaTWA0TeP7l+egJRmpvDOG0u/aUvJTO8rfSMIyMgRyLGR9dZCYEn9aBkX5VB6fk+ZJSHrqoGvw+KQNzB8Vy/B3z2pAYIRif29Hji/m7KsLnfeVNCiP12x2h9yKsrqfG4MBep3uSjwtvZr4gb39wB8/RlBW7Om61Y2TtieNPBHIVDHSUSX1Qc5cWpYWkGkMZar5dKr/SCfs191EztxE4IZUhC75eWMkNqtuj8C4heCHD2IcPaEazKJyNtKTqaSWijv90rF9/j4RGCHggbfSSWxbz/NJO+5lv2Y0438fzZGYZgCQaS5k8oYZFFSXOXQ55WVZvLL3V+ZlbmHaaTf6pC1ZlLXdowuwguD3rJ2MS+jNuOvPYOy1Izi8LYXK8ioSO8Q3MOwKNvrTNSyJ/SWZ6ApUPhSHrW8pfnOLUVIsYADbgCAsEyKZ3zmFy07uMpwypO3NICumCvNTrew6nZpycK2jP5UPBlB5SzSEGXghbwHkQWJABHd2GsfouB5uxwyLCkFRFHT9ZNxRvfVBkvQ/o4yu/fIQ1uk8fsXN3P5hMke229sbGIw6bbtVctFN+YwcX9wghaLUG7qyQuH792KY/1UU5TVko+fgcq68J4e+w8udphHfuppLb8tj9CWF+AdKivIMLPw6ip9nRFNe0vBnStcE+7d6I5Z1B6i9ZLkE8Jo6gLe05Y51r9j6ko9/3V5WHf/tmahFZjIvCWTnyn2OlKI7tGxfhcnPDfkyf4EURkTIVDfTDABZ5nJVQLDu6OjtDkKBS27NpWv/E7xz9Eyklo1QG/aIasa/C/IUVCc1R2Ka8Z/GYzu+p9BS7tRUsvZf+0syHQZo3lBhq/K4XkdSZq3z+FAUhY792tFrRDe3jqPXth1RNy9FYD0rjPJprSmd357SX9tjfjwBW1d/9pdmsrfEjUX834zSKjPmx+Lt5KW+n02tMV+oc4lxZmURj2z/joWZ2xzLKkrNzP3gN5677A3uH/EkBdlFJ0lg8MBf7F2JB44p4YmPU+vSQRWfMOnxus7dd83J4615hxk1oSGBqY/KCoUpE9rxw/sxDgIDsHtjEI9c2Zbfvo9g93o7CWnX3cy03w9y1lUF+Afa3+eIFjauuCuHd+YfIizyREW3t0iN+/WxSgWPiA1Oyx6WmxoY8AnAlFpEUGYe8z74zcvx4LqHsj1zw4pPke7SO/7n4ugYfQKGnF3i9eYkdZj9YQw3DOnC0ze0oaK03hujF7rfsRn/GtQGME/q758+iX8IzSSmGewtyWBvSYbbCIqOZH7mVify4Q4tA6M8/parQqF1cItGze+M2G6MT3JRZq4IJ38YAewpTnfapFqzsuj4Nt7YN4939y9iY8HhJlfulBdXcPxINhWl3h1n94blQ4BwDk3Uh5uU12v75lJcUc5HU75iQuT1vHfnJ6ycvZ5dq/axft4Wj8dUVEG7Pm1QPFmwSDjjoiIiYvycFrbsUM2rs4/w7Fep+AfW/xwoDByTweAL7I0QD+WEeCQvugSbFHz/XgxH9wSgn+AYrGv2/kjvPpzErGkxgOTR6an4B+gYTgi4qAaIb21h8tOZJxzF2xOn6/UtpJlXtZXE2MzI5FieTr7Uo5OwFBCydzdrftno8WhhkTb6jyzzeF0AqFrgerZB1wNGXP0cDz+/jNiWur0JaQPUdTavJTob/gjliUnJNZEnwe61ZTx76Rtc3OIGLom5gReueou96w96mWgzmvG/g2YS0wx2FachvNwYLLqNQ2VZXse6uOVAj08EmtS5KOm0Rs4Qzk7o43UbCU6OuFsLj3Leipd5aueP/JS2ge9S13Dnps+YuPY9l40W3eHozlSeuuhVJkRfz7Ud7uLi6Ot5/sq3yDjoXnNwqDoH0YTwboWtmnFfP8WPb81Da0TUJbZNCz7c8ho3vng17gyeFVUSEqkR9kAwtq9jUd9pT8Lrgdz2RyEzVhyg1+kNrfxBIGQeT/74ANc9dwXr5iRglQpuLHQAmJfbioUzoxoQmPpj2iyCbTui6H6WjaS2Fkcp9olQDTDywmJCa6IxiW2reOGbIyxI28GHSw5wzsQiFEXSsnMCXQZ1cDun+k0q80wByLnnsscc6rElgpBgTrd5JoVAaKQVL0bMgIJ0039IGJIRkZ+BEl6zxEBtpj8vtwuXPXglkXH2dapRtVeB2fdsMJauCfZsDGbLn2H89OlA7j/jddb+upHSgjJK8stYNXs99wx5zGupfjP+XjQ79jYdzSSmGTU3fu/RCdUHl9Xzk/rRNyLZbYfuS1sNolt4y8ZOkS5hiQSp3jU5A6LbA5Bansc9m7+gzGpPb9mk7og0HS3P5Y6Nn2J1046gPvZvPMTdgx9l/fwtjoaQmk1n1ez13DHgYTYu2squVfvIPOxM8FShoHh9NHcN5ZC9ssuXnyShCO6adhMzj0yjbc/WnHZWbyY/bSdoqlr7nkqEkPgH65Q+m8TM0s5k2Pwo7CA40DWel62DeCJ1IJrLj4AEJQqD0cBVj05g5k8f8G7GKCQCWz2SpteEtNeVxiHKdUqLPMvtpFCwtgwn8YpwPPi3AWAwQq/Ty/hywx5mrDhA/zPKMRigTecq7n4lje/2+NG2VxL7Nxx2uf+JXbbX3tIV4ioIbxFKnghwS2SEkCiK7jVyV5xv9KFVhYZQ3LtgC1N/RIuViLC3IHASmdlXMfXy87hxsMJ7d80iP6MQvwATHfq2ddmBvT4UVfLLJ9HMeMIeOa1vZaDZdJDwzu0zSNn9v9dz7v8raquTTvbvv4hmEtMMTots55XCBKl+PnmuGBUDb/e/jonJw5yEwLH+YTzQ5TymdDm/SXP0V01c3uZ0tzd2RQhGxHQlKdBe7fRt6hpsUnfS+NRCkzpp5nyW53juPiyl5LXrp2GttjZwStU1HXNpJY+d+xL3j3iS6zrezV2DHmH36n0ADIrugObJktj9QTHsNLv1ZjkRQgjWzd3sVJF18R1xfLTsIOdMLKBjbzPdB1Zw4xNZaJ8lUd42CL3e177238uKk/g+z1UZsGY3a6tB+r7jbLhJ5+Z1Q1lRnIhFt++fVh3M6xl92GeO4OyEDJ74+Bjzju5kYdoO3pl/iDMuKuJEotyqbSUJ4aVey7EB7nk1g5hEm1PKSVHsWbmw0M2EBy9wSTZOJDBPhg5h+C0VoKVw5rUjEMJ9k0op7UTJGxetNPvyM6pCwHketxDChAg4l/ziG7n3zKPsXuucRquutLB/wyFsFs8uhLomOLo/ziPZUVWFudMW+zDvZjTj343m6qS/GYfLsvkxdR0bCg4jkZwW2Y7LWg+mYxNN2U4FWge3YHB0BzYWHHGpixHAZa0H4+/FxwTAqtswKip3dhrHze1Hk2EuxCAUkoKivDY/9Iab2o0io6KA37N3ogoFTeoOl+OuoUk81eMSx7Z/ZO30WiW1NHs3Y+PdG9ftW3+QtH0n6jHc4+DmI0wZ9Qyv/P4EI4Z3Jd4/nNzqUo/zcAXTGldpHdfQNZ3Nv2+noqTCUQYugq6iTef13PlS3dxXFCeQeywYyjSE2YoMV8Gv7v2QwPd57bmixUHnpuCmoaClIdU4KkpsPDjmGSqKKii9UrLqlXAWndaSKmkg2b+Mi6KP0D7AXjY9+KwSR4qoQy8zD09Lo/fQMt56oCVdTzNz27OZdOzlXWMFUGUWBIfp7smOlFx0Uz6/fhpN/fiVSWq8xkoHgXkmfAhTZ2UTFqWBLOLsSXH8+q6B/Cybo7dTLeF5jT95rv0wrrwrl4fXuG8DoaqS0RcXeSViIvhehOLZ06YW377wMxUlFR5bDHiCalCoLLO5NZMEe0Rm58q9TRq/GaceuhRNSj+fOMZ/Ec0k5m/EgsytPLfrJ4QQjhvbguPbmJe5lUe7X8SFrsSrfxOe6Xk5d276lINlWZzYdadHeCtubHeG2311qTM3YwvfHVtDSoXdjK5zaALtQ+JoHdSCQdEdTprAABgUled6Xc6EVgOZm7GZTHMhkX7BnJ3Qh6EtOmGoJ16o8uJtoyOpsHnuRJ1+oHE+G7ouEei8fetHfLbvHd497QZu3/gJedWlCOyN++q3lnAJIdDDFZTKRmS4JZjLquq8bPzGgt9ZUP07te/kirXRBE3LwLDNThykSWAZHUL11VHISHuX6jxrIHnWAOJM9ciFZTXSshpECEe3jqC0oAwpQWgwwHqcs1oVYqjhtrVVEoCTxkWteVvGXVlE3nEjV9yVh6I6XwMp3WqdHRVL7iAUuwA4MFjHXF73GajfpPIR01CmbznqNFZwaBlvLrud5694mf1bg8hXApgqh/OqXMXa9q148edUwqJs9B9ZypaVIcgTND6KKjH561x+R0MDxrqNIhHBd0PAlfXOtQqkjlACG2xurcrkj5krPBIQoQhHatMVNJtOYFggleWeKwVVQ3Pn6X8L6n93TmaM/yKaSczfhJTyXJ7b9ZP9Blbv01ZLZl7cPYcuoYl0DI3/R+YXbgrk44GTuXvzF+worusSrCDYWZzG3Zu/4M1+kwg8wStGlzpP7viB37N3Ot1095ceZ3/pcQSCaQd/o3dEa17sfZXH5oa+QAhB38hk+kYme9yudVA0R8tz3NIFVSi0DfbcqTswxH03aXeQuiTjYBb71h+k6+BOzB52P79l7WB5zh6qNAsdQ+NpGRjFZ0dWUGhx7T1iOS8c/499N7bzD/Jz9JkCEEKB8Leg4jOk+Us2/lHF+ltAlXXkRFgkpsWlGNdWUPFiInqy/X11K/CWZXTtNZ/h57fiz7kRAMx4JoFjB/wZf2M+xfkGju33Z9xVhQ7SciI0DS69zU5gTtymlsDU/pjrut0QzxO5ORE2W8MNa5tURsTZGpAhqZcQ024CL/70PZn717B3UxBCgOW0Vlzcs44EP/ZRKi/e3opNS0PBQfEFsUkWHp2e6mwwVx8BExGhjyCEneXJqt+QFZ+AdYf9tdoeEXQdBFwK1UuR5e9RkXsQS1V3j+epKIKA0ADMZVUuozXn3XImiqqwYMYf7lt7qAqnjfMulm9GM/7taCYxfxN+TFtn1y24ocuKEPyYuo7HerhvFPdX46uUleysR2AAR9Rga1EKV6x+hxd6X+HUz2nx8R38nr0TcC0Nrm3yuKs4nds3fsLXQ+7yyZ32ZHFJq0G8svdXt+s1qTO+pefIV7+xvfALMFFd6fompccZqD4/HOvwEKSfQE21YJpfjLq/is+zV5G2ahE2qdEjvBW3dBhD17AkpJQUVJcxtEVnVubu5c39CxuMazkvHMPGCgw77KTDszUddB3VBYPJmRUIYYDgyVgMk3j1nluQekOdjZAgSjSC70jDNiSI0NsCaWF0XT6u6/DqXbUExn4TrzKrzP8ymoVfRyF1eParFLcEBuwRGdVbVEXAno2BHNgWRIfeFfQc5L2cXUqFXesDsFS5jvbZVIWxlxc1WF6d8yivPzCPvkP3M/aySrfprcAQnednHuPC9t2pMquA4PbnMzjnmgKMbjOsCiL41joCU/4+svxdnGSI2hFk6eNg/gls2wBBQDA1ncg9Ry7HXjuSitJKln6zCpvFLlAPiw7hkgcu4LKpF5BxMIsFM/6gQVgV+zVWVIXzbxvr8RjN+PvQ3Hag6WgmMX8TNhcc9aiN0KTOpsIjf+OMnFGlWfj+2FqPAt/sqmJuXD+dOzuOY1Lb4YC9w3RtqsQTNKlzrCKPpdm7fCqXPllcmNSf5Tl72FRwxGlutXOd3H60T5GYS6dc4NJy3tbVn4rnE8FU5wWjdfGnsls8aJI/1aNQI21JNxew8Pg2xsR153BZDscq8gCIMgXTKjCKdHOB89UzCszPJuD3UzF+XxfgTh8sFdBbGFhxVQkXrXydq1oP5dLWg1Dqpe7Wz91KWaFnIiAAw7oK5N4y8n4zEpvUMBVXUaqw/JfwenvYoWuipqRbYK1WHN2lTwZLfoxk0bdRvDzLt++DEDprl/RGUc0NIhOqKgmPtnH+tQ0jW2/e35JV88rpc7pvofj6EaE1C8M5/9oC9+cbeD1CtTemlNY9NQQGnN/MmoPatjleG00wakIxS2dHoLlpN6DZdM6ZfCatuyRxy+uTSN2TjsFkoF3vNhhNdtLUqnMij313Hy9c9TZSl47roigKqlHlqZ+mENemrnGmlBKsm8Cy0T4vY38wDfqf7uP2v4RmEtN0NFcn/Yvgzavlr8TekgwqNM8akVq8f3AxmwvsN5jD5dleCUwtFASLj29v6hQbBYOi8ma/SUzuMJoIU509fbvgGJ7teRk3tR/t0zjXPHUpF993HkIIFFXBYFSRRkHFkwlOBAZw/FsqwuVNcUn2blJrCAxAgaWcdHMhoUa7NkIVCgahILAfK7jaiOLm0kqjwDo6hIq3WiLDDGRVFvPG/vk8s2u2U5VO2v5Mn7QPQgdrqeDzl1ynM0PCdVSlIZvSYux6GoANS0I9pn58zdkXFxoICNLo0s+bwFlBIti+eTzmyv7Et41x8KvaebTvaeWNXw7bxbz1cDzFyIpfI5C6YMfaYIeuxxU0DQ7uCKCyou467lgbzNPXJ1NaqJ5wbgYIuhkRMsWxrTR/hztHXle48p4c/AP1Brqh2vMad+MoWndJAiAkIpjuQ7vQeUAHB4GpxbCLB/HVofe4bOqFdBnUka6DO3LVYxP46vB7DDynb938bOnIgvORhROR5dOQ5R8gi65F5p+DtB3zed7NaDr0mrYDJ/v3X0RzJOZvwsDo9qSbC9xGY1ShMLDG4+SfgNWdQ5oLqELh+9S19I9qh1GoWPFtXx1JicW3ipRTAZNi4MZ2o7iu7Ujyq0oxKCqRpuBGPV0qisKtb1zLhHvOYdm3qzl+PI+fo/c0aBtwItwd4sTbkkRSYjVzfduRWHWNClsVrYJacG5iH8LPCeKR4hfY/Nt2x/a2Tn5U3h2LHmuAQOc5KEeqWP7+72QUbiQhOprhlw7G6GdE+miap2mCFb+Ek9y5knMn5RMc5jzb9387zDdvxbJ6QTgA1m7+GPbUiUeXzwnn2gezCY2wNTCvq9W2WG0Kqqq7NTIuL1XYvDyYS2/L9yrozUjry1NXV5KZcgxVTbOTNwlte7Zm7HUj6Tm8K+06bYayxxvsG9/aylOfpTDz9TjWLAwjP8tARIuG8wZ7Gmz29IYu0xuWhHJV324MGF3KY5+3wRQ6FvxHI5QI5w2te8DH7whAQhsLb/56mNfuacnhXXXiX6O/kQl3n8P1L1zpYW9nxLRqwY0vXuV2vdTLkIUTQa8VJ9ebp3YMWXg1RC9AOIz4mtGMfxeaSczfhEtaDWZ22ga366WUXNpqsNv1fzU6hMQ7ypa9QZM6WwqPAjAytiu/eSlnroUqFFoFRXnd7lRDFQqxAeFOyw6WZvF96hrW5B1Al5Ke4a24vM3pDIhyTSRjWrXgiocv4tMjy7AcOobUcevS2tgIvIJgbf5BZp5+Z4N1fUf3YMsfOxzVKNUXRaC3NDn3Y5ISvy8K8P+xCKlCmlZCupLG+vlbiG3TAt2Tve4JkFLw3bsxDBhTSlBotdO5tOlUxRMfpzL9KSs/f9LCLgbeW+VgZlVmlYcvb8vLs44SHm1zXCNdt18TIcCg2kul3aVhvng5Hmu1wtlX5Xu4xoLKymhuGmxBSgMgsdUj4cd2p7F33QEuvvc8pHm9yzihUGDgmFL6jyzjocvb8fjEtrzywxFCIjTHXG1Wu9He9+/G8Oev4S6vl64LCvK74Bf3Tr1rKKF6BdI8005gpOfmka7QpnMV0347xKGdAaQe9McvuD39zn+M4KiujR7LIyp/Bj0b14o2DfR8qJwNQTed2uM2wwnN1UlNR3M66W9C66Bonu15GapQnMqNVaGgIHi656W0C/Gs0fgrEekXzJi4Hj6XQtemvq5qM8znY9jFtI1rOXCkLIeVufvYWpjidKM6Gfx2fAeT1r7PouPbKbJUUGI1szb/IHdu+owZh5Z43HdFzqn31tCRpJbnuVx31vVn4B/kj1ITutCT/ZwJDGD8oxT/H+3CVVFziWpJT35GAcER3ro9O+PiW/Np1bG6ARmrrWC/+cnjRAxXUPJtDe59x/YHcO2gzrw9NYm1i8PYsCSEb9+OoarCPlgtgbFU1ZxPDfc1lytMezyBeV/YvV5mPJuIUKC8RGHeF1G89UAS7z2SyIYloWgaTH86yYUGQNK2WyW9hpRyePOf9rYQRveVPqoBVKPkwXfTOLbfnxuHdeaT5+LZtyWQo3v9WT4ngrvPbc/nL8fjTl4tdbj8oSvqXkuJLH0WWXwLWNaBLAI8l/t7QoeelYy5pIhh47YSaLsWaTu1Lruyah6e3bolstK9QL4ZpwZ2EnOyjr3/9Fn8M2iOxPyNODO+Jx1DE/gpbQMbCw4hJZwW1Y5LWg2kTbBdZFdsMfNrxib+yNpJha2adiGxTGg5gMHRHU9KZCelZFdxOruK01CF4LSodrQLiXPa5oEu53Og9LhDeOoOqlDoH9kWgI6h8bzc+yoe2/E9Fi82/uck9KFPhOfS6FrsL8nk5b2/OnWljvYL4dYOZ3JBE/x0dhal8n3qWjbkH6bMVpPSclHq/smRZewtyWBMXA/GxPdsYPBXrVntqZFTTP8DDCaXy0OjQnhh/iM8du6LVJmrwaw71x1Lid8PRTX1Qg2h2XTKiypo3zeZw1tTfJiJ5LxJBR6rjADOuriAmT+1tD8GnRCEq65UWfxtFIu/tUfdFEUy/PwSWodUIyX88kk0374dQ/dBZlokWCjOM7JhSSjVlQpCSPoMLyc6zsp7jyTwx6woqqtFTQsFwfwvo4lrE0T2MWe9zOCzSrjp8eMktaurJMvPnYS0PeLxPFTV7jHTa0g521eH8NNHMfz0UQxCSKLirDX9nxpeXdWgoNl0rn00jKFjf0erUll9IJKi/B+4oNs3NVv5Qrrr90HyFM3UQJYjy99GhL/pw7g+Qvehh5heeuqO14xmnGI0k5i/Ga2Dorm/y7ku1x0tz+G2jZ9QbDE7xLLZVcWszN3HeYl9ebz7BKfKE1+RYS7g4W3fcrAsCwVhb/te4xb8fO/LiTAFA3avmM8G38ZXR1fyxdEVbsfTpM6VbYY4Xo+I7cr8kQ+zIHMLu4rTSCnPI7Oy0EFqIkxBXNVmKBOTh/lExA6VZTF5w4wGpCi/uoznd//MhvzDPNFjAv6q6xv/iZiVupY39s33OV22Nv8ga/MP8vaBhbzRdxK9IloDdiIYUBmAkCArFfDXTwmZUYXC2Piebtf3GNaFLw+9x8KPlzIndQtpHWSdgDXfhnrc85O+alDpd2YvYtu0YM3Pnjsy+wfqRLTw0lNKQstoM7ZBwZj+9J4qEQJ++TSae17J5Lt3YvjyVbt4eN3iMKftug8o58H304hNsnJwRwD3nt/BXvkkBVo9D5jsVGcCM+LCIh6ZltYgnhAZnQ4ld3udn65Dqw7VbF8dgqJILppcwISbc4mOt1/XsmKVpbPDmf9tf8JbRKBZsmjX+QjnXFNI267VSDMI8zf4mxPoHFWNpgtUd4ps+xXBzv40EMEQ9gZUfgvVK7zMVIOqRUj9GYRycn5LDhjagZaBe8KlgMG3B49mNB3N1UlNRzOJ+ZdAkzr3b/mKEqvZqdqn9qY7P3MrnUITuLz16Y0at9hSweQNMyiy2H/467vFbi1K4Y6Nn/HF6bc7vFuCDf7c3nEsfSLaMHXb19h0zbGPKhR0qXN/l/Poc4LZXLgpkKuT61JLVZqFY+V5KEKhbXCMk5uuN7x3YDEW3ebW2faP7J3sLcngo4E3E+Mf5nKbWuwvyeSNffMBGm3/X26t4u7Nn/PdkHtICIzg7Z9XsWtLKYbhIHOMiDgr0t8+phA4NByNMWhTEJgUA5e3Pp2syiIWHd9OQXWZ3Yk4vg8JgXaRaERsOFc/fjHnW8/h0lVvUmo1o0np08O+EKBZNZ784QG+f/kXvn7uR6zVromKpVpBs+G2q7R9QAX/qlBkn2C0eCNKltVjXZ2mCdYsDOOaB3L4+o04l9u07VbJS98fRTXa3/OfPmoB0s0Pc72PhdGkc+cLmUgaamwURQLem3wqClRW2KNAD3+YzvDzipwCL8FhGuNvKmD8Tasg6Bao+N5p/9pN+7Y+juqV1ApQE8HYH2HqC/7ng16EVJOBdYC3CkEN9Dw4RSRGBFyOrF7mYQsdEei7kLgZTYPElxa83sf4L6KZxPxLsDbvAMcrGxpy1cc3Kau5tNWgRkVjfkrbQGF1udtGiIfLs1mWvZtxCb2d1nUMjef06I78mVunAfFTDFyTPNwnIuWvmugclujzPC26jaXZu1iRs5f1+Ye8bp9ZWcglK9/kwqTTuKT1ICJMQczP3MKWgqPoSPpGJnN+Yj9+SFvncwTmROhILLqVH9PWcX7YQGYu2QIY0Q8EItqZ0daGIcJsiHgLGCSy1ICWZ0DtVYE0So9Eprb9QKgxgNf6TGRO+ia+Tlllf0YXAl1KZhxawnlhvbnM1I/45BhCI0MINQYwfcDN3LvlC7Iqi1FjTOhhKkqJezZjs2p0HtgBRVG46tEJXDb1Arb8sZOPH/qatH0ZThb2uiZYsyiM088ucWq2WB+qqrNrfWvOad+Oec9YCLkzDdyY1jrmYFFY8Us47jTGE+/LRjXUOfmuWRTm1ielPgaMLiU00hOTsx/QE7G0WWHzn6F8e/hiIgOearC+bj8zVLzl9kjeCQyAAGNvlPBX7fOy7EAWXQeyCp8rmE5lpZDfSPA7B6oX0fA2KMBvNPideeqO14xmnGI0k5h/CbYWpmAQCjYPN9vsqmLyqsuI9RJ9qI/5mVs99upRECw6vs2JxBRZyrlh/YfkVpU67WnWLHx0eAl+qpGJyb4Ler3hWHkud276nNzqEpRGeOVU1RCMH9LWYlQMWHWbY75r8w4y49BSgg3+TSIwtdCkZEn2LrTdQaiKQNMl+v4gRIkBpVMFHDairw2tE8n4a8gsK6JVFZ68dke26EpsioHKedm8sf5r9vezG9JJQK+n1ZlXvI3fv15K0I8ljLhsMDe9PJHkpBh+Hj6FNXkH2FxwlEMTAznyob2CSVEkvYeVE9fSQlmxyuYVYfgHhTNkfJ2g2mA0MPCcvkTFR3BbvwcbzG3W+zEMPqsUXZOcGEDTbLBvSyBzPylE6r+R3D2G3NHhKL8VI9xcZkWVtO9ZSX6WEVWFE53w/QM1Bp1V6iAwUoLN4tvnICbRiqbhVcPjjsDoOsz7IpqiHAM5+98lrIeXKJQX1FR5uy0hBx1hOr1mWyuy+HaQlXjWw9RCAdNgnxtJ+gIhBIS/DhWdkeYvQS+oWRGBCJoEQZPtbSya8ZeiOZ3UdDSTmH8JJDiZlJ0qlFo9u7XqSEeqqRafH1lBblWJPV3hAu8fWMxZ8b1o4R/qcj1Aha2aRce3sSJnL5WahU6h8UxoOZD2J4iJqzQLd2z61NFHyGNzRDfzBxroZyQSi26jwFLWqPFcoUqzkllQ6lSqLLP8kFl+mMIy0c6phAoDqBJCtJobpvsflHZF4aTfupotafmIQJXir1rj1gxNCKovicDv5yL+/GEtO1bs4f2NLxOdEMnwmC4Mj+mC9Y2xPHHwZYRtNfe9nkF0vNUReagyK5RWXYu5xIzVqhEeE4pac8dv1zOI6x8t4/MXQ1BUiV4T+TiyJ4Cnrm3Dk58dwz9AotUUIRkMdpO3F25pTS0vLNqXT/+EbmyTxW7PV9cE429pSWZaa3RtByc+8QeF6k4kRAhIaldN+hE/8PLDXFxg8EhgzOUKC2ZGUlmucvV9OSBA1+wpJNUAv38fwYxn7R3kYxNLTorAAGi6wODCpM4OAcIfiQ30MrCssaeGfIKCvZXBvSc3QVezEgYIvhWCbgQt1c7EDG0cLROa8TegOZ/UZDRT7H8J+kYmo3n5FEabQmjRyAaKCQGRHmMbqlBoGVjn3WLTNX7N2OyWwICdIPySvsnt+mPluVy66k1e3TuXTQWH2VWcxpz0TVy15l2+OLLCadvfsnaSV13m8XhNhUQi8Nx7yBsUBO1DYgkL9Ec5UXThp6EPMyJMICJsiFDN9RN/fROIap38e7aTn1kIgKWrXwPTugYIULD1DkSz6RTllvDlk7OcVhtNRp6fcwbPf32MyFi7GLV2Hv6BOjGRn/PDs+dxReJkrki8hW+e/wlLtRVZ9hZX3JnKs18dpfuACkRNc6WOPc2MvbwIP39JWbHCzzNa8N07sdxxVgceuaId5SV1d3pd09m+ZDeTnrrUfr3q5VRETTji3FvOZMjVHzFq0q3oisDSJpKqbnFUJ0chVYXSItVRcl2LC67P9+l9Wzk3jIpy11uWFKjcc24HPn0+gW/eimVi/658/lI8i76N4tt3YrlhaGfemtLKQd4qy09Bp3VVsmiX3WvIVjNu3Udb2tNGpU8ic09HVnyFz06+Sgwi4lOEqddJz9EdhDAiDO0Rxg7NBObvxkmXVwuvhN8Vpk2bRps2bfD392fgwIFs3OhZ+F9cXMwdd9xBfHw8fn5+dOzYkYULG/Z/+zvRHIk5xSixmCm1VhLpF0zQCR2fPSE5qKEj6ImICwhvdHXShFYDeGnPL27Xa1LnwqS6VEOJ1Uyl5lngIIHPji7DpBqYlDzcqeLIpmvcvfkLR3Sn9ve7NqXzwaHfaR3cgjNiuwGwKnefT72XmoqTHVXHbkIYFBrKL2t3O60TidW+MaR618e4qhzyrY7kgQzw7f2s3U636Sz9ZiW3v30dAcF1XbYV8xsghIOInIiJ9+cw/6toinNL+PLpWaxbuZPOkzI4kn8mJoONUU+l8lzn3RhV56jI9jUhfPJ8gue56ZKSvHzG33U2e9Ye4PDWo0gJ7Xsnc/F95zHqqqEIIVi+L4WS6wZgU+vUz6LaRuCGVP74MZOzrix06HDOmVjA1j+D8Q/SaZFopbTQwJqFYZQVO/9kaZrC8zcl89L3RxvMa9pjiWQc9XOE2Qtzjfz4QUyD7Wqx4tdwrrg712tqyh00HUqr/Hnml1F8taYPl/Tfw8C2GSRF1i9Rrn1/qsG6GZ+eI8NeQ/ifhxBNnFgzmuECs2bN4v7772f69OkMHDiQt99+m7POOosDBw4QE9Pwe2KxWDjzzDOJiYlh9uzZJCYmkpqaSnh4+N8/+XpoJjGnCHtLMphxaAnr8g/aQ+9C4cz4nkxuP4YIUxB/5u4lv7qMaL8QRsR0JfAEgrM4a4dD7OkOB8qOY9FtjeoCfW5iXxZkbmV3cXqDsQUwJq4Hp0W1cywLMvh5nQfYtSLTDv4GwLVtRziWr8jdS3ZVsdv9FAQzj650kBiLZvvLCEwtwo2BlNuqsUnfzfJq3UHGJfRmZGxXRKygf8ckth7OdKSVhEk2+unHsN2MFDg6SqvpXhSxNVDT6razVtsozC4msb2dxEhbCtj2eNzf6CcZcnYJv8+KpLJjC9Ymh7FuTW8kAkVI/tjTgfiwAXw4aZ7TTddXf8Ffpy13XLSEdnFM/eIOug/p7Fj/5exVvLN0c51YpIbYST8DFcPb8dm8SgaPXUdolA2Dwe6U+9TnqfbKKpvdaO/OF+0l2t+8FUt99rh1ZTAfPxfPNQ9k4x8o0XUoyjOwakG4I8riC+Z/Gc0F1xcQGKw1SCs56W6UFjXakTodi02z9655bPaZ2HSVQznRvLxwOIvu/xFZQ9Ndw5MWRoDaCuF/QXMjxv/n+Ccce998801uvvlmrr/+egCmT5/OggUL+Oyzz3j44YcbbP/ZZ59RWFjI2rVrMda0b2/Tps3JTfoUoDmddAqwpeAoN63/iPX5hxw/VTap83vWTq5c/Q5nLXuBp3b+yAcHf+epnT8ybtmL/JC6zmmMvKpSFC8/VFZdo9TauN5DJsXAe/1v4JJWg/BT6kLEgaof5yT04ZYOZzr9QPqrJobHdPHZuffTw8uosNWVha7PP+RxXx3J7pJ0qmqiPZ1CE/7StpeqUOgf1Y6fhz/A2SdUYHlCYmAkD3a9gKd7XIIiFIQQvHXbhYzsaSd8QgBVKrKR36DKu2OwjqhLCarHLKj7K0Fz8wukSZTDVahHnUtvg8LqeuqgF3o9rq5BeLQNa0IoFcPbgQCJAgj0mpPILQ3m9pnnY9Psr+3BEuk2utMANZtlp+Ty0NjnOLozFYCqqmren7/ObYmQMFvIjGjJ3ed3YOOSUIeLb+2mqsH+b6NJMmlqDlfclXviCMz+MIYrenXj8Wva8MItXZj7zdWoqiQy1oLJXyMgWCMmqZroePeksTDXyEOXtaUw185gbFb7H0BJQU2jS+NpEDXXXmotwmv2VCm2DuXl329lY4q9MWN8ZAjPXtWS6OACDwSmbv6uIRFBk5sJzH8AJ+/WWycMLi0tdfqrrm5Ytm+xWNiyZQtjxoxxLFMUhTFjxrBu3boG2wPMnTuXwYMHc8cddxAbG0v37t158cUX0bRT46TeVDRHYk4SmtR5etePaFJvEFHQpO5UGVP77yrdyuv75mFSDA4b/ki/YK8/dapQCDH4N3qOAQYTU7qez20dxzInfSOz09ZzvLKIBce3seD4NnqFt+b+LufRpaYk+oZ2Z7Am74BPBZ9VupVVufsc1U26j5VAmpSUW6vYXnTsL43DaFLn0laDiAsIp3Wg55SdALqEJfFOv+sINQagSZ10cyGqECQERBDkb+L1W84nLbeY1buPUmat4ku5CFsjmvthFFROjUWUahi32kXXAe/kUv56Evgpzi0FbBIsksC3curmqAgSB7Yk16+CcGqq1FTX3iv1oRog77iRyt6JbstnNKlwvDiUFfvbMKbbUYSAL15NoNuASvZuDvA5qqHrOjaLjZnP/shTs6fwzZfL0ALqaSysGn5HCzBklWLILkUtqUIAuZh49sY2fLlhHzGJVrcVRVfem8Pcz6Mxl9elV0z+GkPOLqFFopXqymqiIuZxxoRAfp8ViaKArknyMk2oBsn4m3I5sC2IfVtObMcgST3gz80jOpOYXM3I8UVoumDrnyGcM7GckVddiAi+CyH8IeQ+ZPA92J3u/IgRRp6+AR6aaMVm0wgO8APLGnvXAY8QIMJAFlNnf6wCGgTeCAGX+HTNm9GMWrRs2dLp9VNPPcXTTz/ttCw/Px9N04iNdW51Exsby/79+12Oe/ToUZYtW8bVV1/NwoULOXz4MLfffjtWq5WnnmpoTfB3oZnEnCQ2FRwhp8oH624XeOfAQrqHJdE+NJ6zE/rw2ZHlbretdXX1U5suuNuQf4j3DixusHxXcRqTN3zERwMn0zUsic5hibzd/zoe3vYNZbYqFyPVQSAoqVcB1T2sJfMzt3rcp1VgNAGKkbs2f86u4pPoBVOtY1pegbKjCoIltl6B2AYHgSocKbGb24+mT2Qyi49vZ/rhPzwOJ4Gz4nsRaDDxyZFl/JC6znFusf5hTEwexmWtBtMqJpyrRvUFIDJV8Nq+eb7PWQjQJNVXRzpIjJpqIfjedKqvisQ6LMROZDSJYXU5/t8WoGbYwwFS2MXK+8frTFz7Pl3Dknimx6W0Dk5EGgfUaCwakkgp7aLVNb+FYZ0Y7tGJTxU6qw+1ZlTnoyz/JYLjR/157NNw3rr7OId2BiAUidRr8mES3EURdE1n7S8bMZdVsmf7Mcdy05F8gv48grDWEb/6xvvte1QSm+TZgdg/QDLwzFKWz7EbAYZHW/li3X78A3WHUd+HTybwx6xIkAJdw0HAbFbBL5/E8MDbaRiMkl3rg6lrKyCwWQWaTXJ4dwCHd9dFu3asDWHJXCuTnkqn84AO9vkKxe64Ww8BJiOY7N9RqfrikyQh7DmErEBWLgJZCoYOiMDLEcYePuzfjP8XaKIwt8EYQHp6OqGhdZWjfn6+azM9Qdd1YmJimDFjBqqq0q9fPzIzM3nttdf+URLTnE46SaRW5DmaITYWFbZqrlr7Htev+wCzrZoJLQe4HElB4KcYuaHdqCbP06ZrvLL3V2TN/+pDR2LVNd6odzM+Laodnwy81eu4Ekl8QITj9biE3gQZ/Dxek4taDmBvaSabCo80uqS6FuqeSkKvScH/3WxMK4sxLSol6MUsgm88hki1oOUZuCl6HDe3H42Uks+OLPfpXXpn/0Ju2fAxnxxe5kTOcqpKeGPffF7d+6tTKXyTmnaqAq1LAFp0XSRBOW4l4LUc/N/O4YG253LRrwkEvZaN4bgNUROdkUEK5sfj0XrYtTAHSo9z84aPyKkqQYQ+Ahg58Stdm8GZ/nQC1VWqVythCVhsKkt+imD+18N4afFjtB90C2/+eogH3kqja/8KYpOqiWhhbeAhcyJ0XVJeVE5tvMOQUUzwkoMIq+aoGjtxNiER3qNaug4h4XXb3ftaBv6B9u7YBiMU5xuY90W0B98MyddvxHL3q+kIF+Y29v3Eibuw5fcd3Dv0CTb/vsPrHAGEIRmMvXH/MytAiUL4jUYETECJ/BglahZK2PPNBOY/hlpNzMn+AYSGhjr9uSIx0dHRqKpKTk6O0/KcnBzi4lxHduPj4+nYsaPDogGgS5cuZGdnY7H4pu37K9BMYk4SQarfSQtT95VkMnnDR1yQ1J9JySMaCHfbBscwY+DNtA6KbvIx1uUfbOAHUx869gaRqRX5jmXJITH0CG/l0YAuwhTE6dEdHa8DDX680udqj7qYX9I38mPquiaTP5FtJejxTKjQ7TdCCaJGbKvk2Qh+4Dj6n8F89/0eLFYbOVUlHKvI8+ldksDuknS37+lP6RvZXnSMjQWHeWz7dzy07dsmnQOArX+QQ1OjtzRReXcMlvviWFWyn0dm3MXMI9O44NnzqbwiAvNDcZR9k4xtUN2TvyZ1ymxVfJOyCmHshoj6FgzOXZvzjht56fZW/PZdFEKXKMVmjwpAiUJs8Bn0Om8u76x7nX5n9gJjX0yhZzD28hLe/OUIL/2WSczIwJrmiO5hMBkIjQ5l5LDuKEVmAjel27MnHvbJOOL9qVFRYNSEQp758iiv/XyYwWeVOnGzdb/X6WpcQ5CT7oe1WqFLv0pM/pKE5GoiYzxHgHRNR9d0Xpn0Hjar93YGACL0SVyRy9qrIEKftfu0/IthtVhZP38LCz9ewrp5m7Famt6Vuxn/DphMJvr168fSpUsdy3RdZ+nSpQwePNjlPkOGDOHw4cPo9b5cBw8eJD4+HpPJtz52fwX+3d+e/wEMjens1WnXG2ojIe8fWMwHA27i2rYjWF9wiCrNStvgGLqEJp60uO94ZZFPpcw5lcVOZOmBLucxecMMJzfc+niw6wUN+iL1jUgm2OBHsRujvVRzPqnmfJfrfIHf3GKwSlzpTYUEzFZMB/Io6W5g+Y4jdOkS1XBDN/B2fRQET+38keyq4ia3M6hF1d2xVN0ZY2dOat37m1Zhd02NaxNDxYWh2Pq1cHscTerMy9jC/V3OQxh7IKJnI22HwZZOSaHCM5PncXhbKqpBBQEBu7KoGNrW5VgCMKoq111yHhH1yrftrq5vI0ueYc2uzUyZNRbdz0YYW90SEsWgMPqK1vjxM8MuaE3cc8epzvVuPJibYWL3xkC69DW7NJ6r5V+d+1bWzK3hNuYytUYH4/lY5nKVa6Zk0bW/Gf9A+8AHtgfw9RtxbFzq2shRSklxbgnr5m1h2ISBXs9HGLtD1PfI0hfBWs9bydABEfIgwm+41zH+SSz9ZhUf3vc5Jfl1711oVAi3vnEtZ04a4WHPZjQK/4DZ3f3338+1115L//79GTBgAG+//TYVFRWOaqVJkyaRmJjISy+9BMBtt93G+++/zz333MNdd93FoUOHePHFF7n7bu9NVv9KNJOYk0S4KYjLWg3mu9Q1J/UZ1JFsLjxKdmUxcQHhjIk7NeFkXeqszz/E8uw9PkWMwkyBTq8TAiLoEBLHnpIMp+UmxcDULucz2sU8V+Xtd0tgTgWMq8rcWtzXwnS0AK1XIkezCjijbzuCDH5OVVRNhY50lJCfDIFxoL7AttiGcgT0EMmWhAx6t0+g0FLuVSxdoVVj0zUHmRSG9mBoT3gCfLB5ONuW7Wbjgi0U55Wy8uf1WJPCsbSpsa6vZQG6vXvl89ePcyIwtRDCnwLxOFN/+ASbpiMDDVT2a0ng5vSGp6RCcIiFq++chyz9GQMwa2EsD18awv6tJ4ppG+KDxxN585fDgHQiMrUExhufT2pX5VWELIQkoXU14S1sTr4w7XtU8tzMFN58IInfvnNNflWDStq+DMA7iQFqomTfIG1poGeDEglqu3991dHy79fw8jXvNlheWlDGq9e9j1AEYyb+u0nY/wr+ibYDl19+OXl5eTz55JNkZ2fTu3dvFi9e7BD7pqWlORl8tmzZkt9++4377ruPnj17kpiYyD333MNDDz10UvM+WTQqnVRZWcnq1avZu3dvg3VVVVV89dVXp2xi/0u4s9M4Lmo5ALA/qRuEisAuLq398xW5TRQJu0KxxcwN66dz75Yv2VGc6nX7loFRdAyJd7y26DZu3/Qp+0uPN9hWkzozDi+h2NKQrBwqzfK5RLspEF64iACETUPXJQEmIybFwISWAxv1PvydUDItBDyWTejVKYQ8kUL1vZt5YPgTnHP1q1SmK14NDkONAW67hAsh6Du6B7e+eR0GkwHNqhH8xwGCVh5BLaxJLWk6howSQn7ZSVsPBo2/rNmNTaujwpV9k6gYkowe4Cw273V6GW/PP+Qk0PUPyOPV2UdI7uLdIuDI7kDuu6ADO9YGN1jny33/tDPKiIyxui0NV1TJwDGlRMbaGhjbqar9ktz1YiYhEa5TRrquExDc+CpBYWiFMA2wu+L+ywmMpml8NOVLj9vMmPoVmu2fLa9txsnhzjvvJDU1lerqajZs2MDAgXXEfMWKFXzxxRdO2w8ePJj169dTVVXFkSNHePTRR500Mv8EfL7THDx4kC5dujB8+HB69OjBiBEjyMrKcqwvKSlxhKH+azAoKg93G8+c4VO4pcOZXNJqEPd1PodFox5h+sCb6Rae5PNYEaaGP9xNxSPbv+VADQHxJWrQPiTO6cd1afYuDpdlu9xXkzoF1eX8nL6hwTo/1XjK+0D5VdfdEPVkk0d/FilAjfBDl5KRvey+Lje1G+UoIfcEAX9pDxIB+AkDqhQYtlbgPz2X4DvSMG4vc0qPGXLL0X7YwoYP9mDNcF+RpiCcHJfdQbNpLPt2FbpNR0jwO5iH6VghWDRQFWytIig7uwsvf7IIm+b6s7LpQLpTc0qEoKp7PEVX96Pkgu74XdaKL9fv4+VZR0lMdhb6CXSMRpg4JQdfcHRvAI9c0Y6r+nXhvgvac3i376RBNcCUt9NQVDthcVqnSkLCNG599rhbQiQEqEbJ6Alu6qMlDBk/ACklRTnFFOWWnNTnXerFSOtepC3D+8Z/E3at3EfBcc/14UU5JWxfvtvjNs1oBORJ/v1H4TOJeeihh+jevTu5ubkcOHCAkJAQhgwZQlraSZTI/j9DYmAk17cbyf1dzuWKNkOIMAXTO6INnw66jS8H3+ExOiEQdAlNpGWQ7/oNT9hbksGWwqONSnksz9nDhvxDjteLMrd7jF7oSBa4KKceFtO5yVVHrnDhou18c/unxOTZDQHVC2M8ppNidDOfZ87m0fKjtImzp00CDCY+HHAzd3UaR6ihYboEavQ0mRYo11yaz52KZ2cJ3O13BmGT0wh6/DimuSUu9T21ZCr4zyMYd4Xi6m1UhUK0XyhXtxnq9biV5VVYq+2RBSmg7MxOVPZNAr+6fI30N7LZUsnDny5wanZZN3c376mqYIsP5dzxWbTwUB6tqJLTx5YQFObrZ0NSkGVk35ZAAmqqj3xF3xHlvPbTYXqdXl43TYNkxIVFvLvoIPGtPVdTaJq9CeWJEIpgzDXD2bBgK9d2vIvL4m/msribuKHLPSz8eEmjyIzUMtGL70PmDkYWjEfmj0LPvwhZvdL3E/2LUJzrW0S4OLfU+0bN8IpTaXb3X4PPJGbt2rW89NJLREdH0759e+bNm8dZZ53FsGHDOHq0Yd+S/yoyzYV8k7Kajw8v5fesHVRr9h/1LmGJ3Nx+tMt9ags67+w07pTNY3Xu/kandFShMKuek3CRpcIrGSlxoX1pGxzLsBadT0n6xq/ayjWz19PqeBHTH/yG6NwSzrl8JJaRwQ0eQCTQQpp5zbSGVuWFTFi3GCrr0hf+qpFrkoezZMwTvNrnajqH1kVmjLqKcW4RIfelE/xwBiK/JpVgk/Y/wOBj75pBUXYfEbXe+de+FzeEDuW7i79EM9uoGmc3q3NniCsApdqG3FfAWcb+BKjOFQADo9rz6eBbifTzHr0LCPHHP8ieKrK0icLaJtJ1bkbAsm2HWbW74Xe6f8eWHl2lI4Oqalpuuoeiwrhr+2Ew+nItBULYpxkQrLskcrU4kTtYLOF0Gjyel+cNYdbhnny6pR8/7jvO1PfSiWvpvbpGCEFFhWq371CEw8Jj+CWD0XWd9+78hKyjdVGljINZvHXLR1yRNJlfpy2mstxz2kxqmciCS6BqMdQ3S7TtRRbdjKyc73WOfyWik3x7mIpOivyLZ/IfwclGYf7D0Rifhb2VlZUYDHWbCyH48MMPufPOOxkxYgTfftv0UtP/D7DoNl7a8wsLM7fa9TBCYJM6IQZ/nuxxCSNiu3J925GoQuGzI8udmixG+YXwaLeLnHoYnSysutZoCqFJnV3FaewtyeDX9E3kVpU4egi5ggDCjIFM3TqTTQVHkECfiDZc2WYIz/S6jKlbv2ZL4VFUoTieUH2J0KhCQSLRpaTaz8jtL1/F9Ae/ISm7mM8f+YGQ1fex49n+HPluL8ZfClGz7IQjMkbntYo1JJaXoLdNRlm+DAJcR11GxHRlUHQHSixmqjUbjw96lqx9+SBBLbcQcuMxbP2D0Lr4gy4xbDNTOTEKega6HK8+Cl/eT1xJGebzQ6nsYEAxqPSPasuVrYew4/V15J/jT9Xl8SgHqvBf7PmJVwpQSqsYENyJx047jx3FqVh0G+2D40gIjPC4r9M1VVXOuu4M5k3/naqusaBLl669AKoi+GnVLkb0dP48XjS0O58t3ojV5rrfenZJcP0iKzcwcstb93LNs5IvnpjFL+957oArpUAoktfuacmL36S43a6OWymgxODfci5CCQcgMhQiAVk9DK3gBnQpObEh+YkwGCQL9NMxD5KopVXofgZOu6A/Z/brzOPnv1wzuYb7FWYV8/5dn/LDa7/yxopniGvjuuGkLHsd9GJo4PZsH1SWPgH+oxHC9ef3r0bXwR2JbxtLdkquy+iSENCiVQt6DOvyD8yuGc2og88kpnPnzmzevJkuXZw/tO+//z4AF1xwwamd2f8Ynt/1E79l7awhxNKhHSi3VfHQtm/4YMCN9I1sy7VtR3Bpq0GsyTtAidVMYkAkA6Lbe42aFFnKmZuxhaXZu6nSLHQJS+TiloPoGdHK5fYdQ+ObVPZdpVm4bt0HPpUPSyDdXMDxyiLHthvyD7E2/yC3dRjLB6fdyLaiY/yRtZMyWyUtA6PYUZTK5kLPkTtN6pwW2Y7NhUeRSHJbhHLna9fw+SM/0CIjB0aPZdqSxbw8OZo/ztuBLNNoUVjKm898T2J5CVpyMuqKP+EE+22AMmslX6es4uf0jZRYzRiFynClPVl7s5y2EzoYN1Zg3Gj31lENCrYtZqq9kRibJDW6FP85+SjL8wgE2nRryTPLJxAaFcJTue9RdXXN02uwD5EyCdKkEh0WRIDBxKDoDt73cYMrHrmIlT+tpyA8wC2BAdB0SWpOQz1Ei7BgXpt8HlM+mocuJVpNyklVBJouSUy6BiE2epiBCv7nI0QAQaFwxzvX07ZnKz6493OqKtyrtXVNsPXPUD58KoHbnzuOzYaj23WtQ+/s6dGMu7KQIwUJJHb4kJgaAlMfwm8Iv+69m9MSPnc0uXTF5TRdsP5IEvsK46Fn3fKVWblUvrwXRVXQ3eiGapF/vJAnLniZGTveaCDilXpJwwjMiZAV9m0CLvJ4HLe7S2mvhJKVoCbYWyX4sp/tKNL8HVg2cceLKk9cpditGeoRmdrzueu9G52qV5pxMnBl/diUMf578JnEXHTRRXz33Xdcc801Dda9//776LrO9OnTT+nk/u2w6Rpr8w+ypzidxVmuXTwl9o/WjENLmT7Q7s8RaPDjzPieLrd3hf0lmdyx6VPKbdUOXUK6uYBFx7dzXduR3N5xbIN9RsZ2JcTgT7mtyucoo0BQrdujGt4ITP1O1079oWqWfXjod/pEtqFvZDJ9I5Md6z87spythSleIzIZlYWMje9J59AEEgOj6B3RmvARU2HkSDh6lKAx43huxQru6XQ2e3evp/+DNxKYXQxt22JZ8huLyWLv7k0YFZXB0Z04vUVHyqyV3LT+IzLMBY7jW6XGn1l78Vr4KwR+UsWXIm3dX0H6Cax9A5GhKkfyCnnjlg95ZNZ9FJ5X92SttzKhJRlRMq1uU0oIQVD3JAZ0ck1WG4PohEjeXfsClz/7FWVumjFCTYQtyPVNb1iPtsx+6lp+/HMHq3YdxaZLerdL4PKRveneJg5ZfieyvGFZLqggQhEhdzktPfvG0WxdspM/f1yHdKHDqY9fP23Bsf3+XHxLHn2GlaMokr2bg/j54xasWxzCMlsrSkZHcnT959zYcTQ3dxzTgED06nI545+vplfLbEZ1OcL4vvsJNFmx6QoCiUGVrD3Uisd+HtPg+IoQHNud7pXAAOg2nWO709mxYg+9z3A2IEQ7jkcCA4ABaUtr0m1JVv2GLH8PbAftC0QAMuBSRPDdCMW1/w2ANP+ILH0cu8pA47Th8PzXoXz4RIKTCWFihzhue+t6Bpzdpwmza4ZL/AM+Mf9fIOSpLiP5F6O0tJSwsDBKSkqceks0BSty9vLSnjkeXXBPxG+jHm109VG1ZuWCP1+lxGJ2e+N/odcVTqTIpmu8tncuczI2udz+ZOGnGEgIiCS1Is/tnFShMCq2Gy/0vtJp+arc/Tyw1bdSfFUo6FJnatcLuKTVIPvC9HQHkaFtW5g5E665xvF668+fc3/ucsxatSO6pUmd1kHRtA+OY0Xu3oYETZOETExBKfF8Yxnw9mj+6OhFyC4l4rgVGWMEY90tSORZubzLUL7Pd67mMqwpJ+iFLAfZdRoKqOoZz2MzbufsAZ09HxcoKjOzJzUHAXRPjndLRL76YzPv/LzKY5pw6mUjueKMxt+kpJRg/gZZMQ30groVpiGI0KcRhtbsWrWPX95byK7V+5GajinQRF5avicjYa9QVcm1D2Vx+Z15HKoM5a1DfVELRtEnLJnOrWIY3LU1ak3U4JXvlzHrT/tDh7/RypndjtC2RSGVFiPL9rXlcK57PUjYrG0Yin3rJK8aVK54aDzXPXeF03KpZSLzzvCyt2I3wwu6wadjOcau+ApZ9jw0SASrYGiLiPweoYQ03M+yA1l4Ga7uhFIKDu8KpaDyFaISW9OxX9t/fYn4qcKpvGd4Gr/lh0+jBDS+bL8+9Moq0m97+i+b678VzWZ3TcC6vIM8tO2bRrcbKLdWN5rE/JG9yyNRUhB8nbLKicS8sW8+v3ggMCoKWr1GgUqNk6+3sxEILmk1kNs6juWm9dM9RlM0qbPDRXPH01t0pIVfKAXVZV6jMbVk49W9c+kUmkCP8Fb2FNGKFXVEZsgQ+8Zt23Js/o/clTbHkUarT1bSzYVOLRWcoAos54fh902hy4iIoiiEx4TyyORr2b7uHfKqPVdkyMSGFtwyysD3eRsaMBXbkGDM98US8GEuskraGxjXTLuqRxxTP7jZK4Epr6zmtR9WsHDjfjRdB6tG4LFCOoeFMnZoN0ZdPoTohDoB5vjTu/Ptsm0UlFY4UkKOS6EIWoQHc96grh6P6Q5CCAiaCIGXg3UHSDOobREGu83Aty/+zOePf+dTSqYOriieMzQNWiTYBbvt/Ep5r/uf3P1tEB+tzkbTJbERwbw2+Xy6t4lj6mVnEBsRwhe/b6LUDPO2eyeItbC0i0bdmu4+cnYCdBf9D4SaiDR0B9se3D8+S/BvnNBfarnIshfr9neCBrYjyIpPECH3NdzX/BW1EZgG8xWSDj3L6Bh8ABHc9P5tzfCA/0AkRtM01qxZQ8+ePQkPDz9l4zYnNBsJKWVNJ+jGfWKMikqUD1UkJ2JLwVGvZc77SjMdVVA5VSXMSd/o8Sk7KTCSuzuOY1BUB06Lasek5OEN+jW52zfWP5xggz+qD5U6Bhc6H1UoPN/rcgyK6nP1lCoUvj+2pm5By5b2CEx9zJzJTJs9TeWKjnlzva2+NBKtj13vIuqJJBSDgl+giafnPEiwfwBfn34n0X4Nn2QdcPeEqrhvGmQ9M5TSb9pSeV8s1ZdHYh7ViqKr+1M1NYL2XcM9z9tq49Z3fmLBxn1ouo7pYC6RMzfhv+wQx37dysdTvuLq1rfxwb2fo2n2G1RokD+f3H8pbePtEQdVEag159w+MZpP7r+M4AA/x/iLNu7n/V/X8OmijRzNKnA6vqbrrN6dwiOfLuDmN3/kyS8Ws/lgOmBAmPoj/IY7CMz25bv5/PHvAHwkMBKhSHoMqqB9D7P7Mi4kAUE6p4+zi6QVBQSSh85eZSd1QF5xBbe8NZu03CIURXDdWafx+8uT+fSBy3j5xrPp1S7BMVYteiRlMXnERpIi7OMKgF6JSKPqU8NhzabRbYhrgiRC7q39l6u1EHAZQk1wsc4DKn/ysoEO5u+Qrr4L1avwnOLSkdWrGjefZviO2i7WJ/v3L4aqqowdO5aiIs/+Q41FcySmkUityONweXaj9lGFwrj43gR6cEN1h0rN4lNFT+0WK3L2eNxaYu9dNCymCxPb2i3Dl2bvcmhhPEFH0rXGMG5oi04cKcv2mE4aFuP6B7xPZDJfDr6dL47+ydLsXV4FyJrU2VhwpG5Bero9hVQf11zDrmcvRItqYkjWKKh8OpFxe1qR88NRMg4exz/In1FXDmX83WcTn2y34o7wC+aHYffx2p65LMra3rRjudKjBChYRodCqYr2ZwTEWFAibV7f+0Ub97M31V7qazxWSMjyw3Vxi5ooi67p/PLeQoQiuO3N6wBIahHO949NZNvhTLYezkQg6N8xiZ5t4x2pgtW7U3js80WUmasxqAq6lEybu4YzerXjuevsUYJ7P/yVzQczUBSBrktURTB/wz7G9O3AHcP78Ptny0k/kElAsD8Zh7JQDQqazbcITESMjQuuz+eSW/JI2efPAxPaY7OcaK8uCQ7TeH5miqP/EdiJTKuoUnom5bAzIw5dSiw2GzOXbOGxq+x6F5PRQJ/29s/z2P6d2Xd0JVt3voOf0Ua/Npm0jirFpgtuGr6FFxeM4JetXenaPoHt53UjdPE+hNmzJ05My1D6n9WrwTopbWDoBKHPQ9mLdhEvBuwhOGknMKFP+nSNnMa1HcOruFMWgywHcWK6wZf35BS02WjGfxrdu3fn6NGjJCcne9/YRzSTmEaisT2BVKEQaQri1g5nNul4Ft27p0WAasRftbu6ltuqUIRA8yIwqNDqJKqF1eUeS6lr0cIvlH6RdnHyhFYD+fbYaiy65jLyYU89ue6GCtAuJI7nel3OUz0u4eYNHzXozXQiHMfwoIl5a+rn3PLK1eS2aFo+WDfADXdeQquH6hpgWi1W1s3dzO9frMAv0I/BF/SndZck2obEomSJppn6uYjWSB3QQdsRgkioRulTRoBqpF1wrMeh5qzehRAgdUngxlS3iRcp4Zf3F3HFQ+OJiA2vmYagb4ck+nZo6Ci982gW9334q6PKrr6L74odR5jy4VwiwoPYeigTwGGOV5ueWrL1EKs/W0HIZrsQ1tf0UXI/C4k3tGD14TZkawb+CC4i8tBuxvQ6zOs/H+b9R5I4tLOuQmzcVQXc/Upmg/YBtYgLK2NnRpxjbos27neQGCklBVlFWKutRMWb6BR0Px0HliPqOSkaFPv5PHren0SFd+ePKX+ijetA0VV98UspoPW+fRQcNyKEdJArRZEEh2o889lWFG03qHYiI3UzsmIGmL+1kwkAQx8w9QXhb9eq+I9zisBI22GwbLa/MJ2GMHiwYVCC8V6hooCrSiVTf6j+E/fRGAVM3p2hm9E0SNnQ66gpY/zb8fzzzzNlyhSee+45+vXrR1CQc0lFU7Q8zSSmkYj1D/N5W1UonBnXgzs7jaOFf9NurNWa9wiJpV673laB0T5VFsUHhDtex/iH+XQrntLlPMdTeqx/GG/0m8SUrTOp1mwOkqEgUBWFl3tf5dQN2x0MisppUe3ZV5LpMarTP7JtQwKzYoWTRibx6FGmP/gNt77qmsh46uKtIBgR25VW9ea8bdkuXrjybUrySlGNKlKXfPrIN5x+4Wm0fObUVWZICZgVZKERtV8pIlhHQTC+5Wleo3fZRWVICUpJFYYibwZrktU/b+D8287yOqePF663p+lrL5emY0wvRimvRgYYWW/VwIthXVW3OAK3pCNwnT6KirPSb2QpJj/Jkd0B7MhPZGv/zmzeqVCrg9mVEcuujDjm7+jIm1cs4v3FRzl66DQ+e17n+gd30LZrtUcn35JK5+tnrrai65JVs9fxzQs/kbLLrtsKDjdy7jVBXHVPGf4uq+hVLmq/hl/SJcErBOVndGDg2eW89sVeVs0LZ/5XUaQf9iMoRGPUhGLOnVRARAuJrPgYYXrfTmAKr6nRwdS7FrYdYNuGCH0eEXiZY7HU8pElU8Cy1mkW0nQ6Iux1hNrwuyX8xyHNMxssr38O+J2BEA01WyLwWmT1Mg/7CkTgFR7WN+Ok8B/QxACcc845gN2Spb44XEqJEMKR8m4MmkRiZs6cyfTp00lJSWHdunW0bt2at99+m+TkZC688MKmDPk/g/iACPpHtvVYJhyo+vHeadfTOqgFocaTM6syqd7fovrakxGxXQk1BlBqdX1DU4XC8JguDoGxlJLEwAiCVD+n6MyJaBsUyxlxzqWiA6La8+uIqczN2MKmAnsao29EMhcm9Se6EaRtQssBfJ2yCildm6hpUucaQ7JrAgMOIlMxdDBJaZkeiIzkkpYD+Sl9A0qNAZ8QAk3q9I9qx1VthvLcrp9Yn38Iq9WGeXkupmgLah5o1rov1/r5W8gvKUGfqvvWkdANFMAgDFixIYN1RHB1Tek69Ixo7bJ03um6aBrhVRpFueVQUeX9eKqgvNh7JNFcZWHtnmOO98J0OI+g1Sl292Dsz/pVXWKoGN7e80BGFWtsCKZMZ0M/vwCdO1/KYMzFRQilLrt28fv9KSusvZ7O/11/pCX3fnsOheYACsqCiRkaS8uOm72eS1iA82c6NiKYH1+fyycPf+30I1pebOXHaS3YsSaIV388gl+A8ydRERqRUbtRlO74HcpHKa1m1DsZSAQjxxczcnyx6wlUL7VrUMyfNSQw4HgtS58Gv9EINaqG8FwNmotKOMsGZOFEiPoZoZzAtoz9wTgQrJtpGFGx+5CI4FtdTlP4DYbg+5Dlb2FXl9furwKyhjh57z3WjGZ4wvLly0/5mI0mMR9++CFPPvkk9957Ly+88IKDOYWHh/P222//vycxAPd2Ppeb1k/HorvWLDzY9QJ7Jc0pwKDoDqzNO+CWZCsIhrTo5HhtUgw83v1iHt72DeDskKsKhVBjAPd0srPhZdm7mX7oD45V5Lk9fq378IPdXJsZRpiCubbtCC5vPZjfsnay+Pg2lmTvomVgFONbnsbg6I5eyzHjAsJ5ofcVPLrdLvqsjSTVGu49GtqPbuOvcU1gatGyJX4rV5M/ZABJmXlORKbW0+auTmczMXkYVyUPZW7GZjLNhYQaAxkb35NMcyGTN8yoScXV3GiGBGEZEYz/9Fz85tbdiHVN5+CKg7S5vDfpbSob1Z+qPiSCsfE9aRkUzYLMrXbzw8BIJrQcwNkJvTG6EVtLKZnzzkK+e2UOxTklhDnG85xM0Gw6Ce08p6fAHq1wEJijBYQsPeR47Rjf11NuYKoneXzGMfqNLKO28bYQsD0tjrRCTw7Ego0pSY5/F5uLUVXpkUNqOgzrmMrvezrUTEUwrn1rPrn5Y/tMToi/67rgwPZA5n4ezaW3N/xOKEJ3RKaMOWWEFBchvD7+akhpAfM3eL5oOlT+DME3Q9WvoLlzJ9ZAOwpVc+GEyIgQAiI+QBbfC5ZV2AmIAGwgghHhbyKM7v2pRPBtYOqHrPgKrFsAA/iNQARegzB2crtfM04BToUw918u7AUYMWLEKR+z0STmvffe4+OPP2b8+PG8/PLLjuX9+/dnypQpp3Ry/1Z0DI3n40G38PreeewoTnUsTwqM4q6OZzWIWJwMzk3oy8eHl1JurXJJmCSSq9oMc1o2MrYr7592A9MPLWFnzfxUoTA6rjt3dDyL+IAIfknfxIt75njNoLcLjmFK1/OdDOtORH5VKbdt+oTUinxHyuZYeS4rcvcyOrY7z9VUI3nCGbHd+H7IPfyYtp41eQfQpE7fyDZc1qIPXYef45HA7C/J5JeMTaSbC2j58RNMnvwCSRk5fPDwt1z9wY10iLE7JY+ItZcNJwVGcXvHupRKanket238BIl01hIZ7Fen6tYY1ANVGA7Ue6pXBcW/Z6JMjvJqW+YOEsmC41s5N6Ev3w+9x+s1qsX7d33K3A9+c1pmGR6MqNQxbDF7bI75xk0fsvTbVVx873n0GtnN5TZhwf4E+ZuoqKwmcP0xl+TIkFfualdn6BJDgbM9QPeBFQwYXdZg0+1p8ShCR/fUnvyEWaiKZwIhBPgZ7elYRRG0i4/Cf38OiuJenyN1mPtFQxIjpUJhQWsn+5XUA/4MPbek4SBOk0xEYEHqBZ63QyBth+zDV/5MQ5+XE7at/MllekcoIYjIT5HWvTVRoCqEoaNdayO8FxYI0wCEaYDX7ZpxaiGkh+K7Rozxv4BVq1bx0UcfcfToUX788UcSExOZOXMmycnJDB3qvZntiWg0iUlJSaFPn4Z6AD8/PyoqfDd++19Hp9AEPh50C+kVBWRVFhFmCqRjSPwpN4EKNvrzbv/ruXvz55RZKx0/a0rNM+AZsd1YnbeftIo8Rsf1IMBgz3f3j2rHJ1HtyK0qocxaRYx/KCE1qa0yayWv75sHuP6ZVBD0jUzmns7n+HROj2z/jgxzYc14NQLPmv8uy9lN8pEYJndo6IAK9jYHh8rs1V7tg+OY0vV8pnC+80YPPgivvgrLljkRGCklb+5bwKy0tY6ozRYhWP3iJXzy6A8EPfQIi8+9nSAvupLZ6evt5+hOGWeTWM4Px3CgruGflBJLaRVW2VQKUzMOMP/4VvaVZvL1kLu8lp0f2HS4AYHRWhqxjg7Bb1YR6A0jMrLmfiiwd7PeuHAr6+ZuZvJrk7j0gROuNWCrstJequzJKUMtc51iNBSaMWSVYosNcd3CQJeYUgpQTqjgGTWhCJsVDEZXV8J3SAQpeeG0ji5220FBSsHh3Ej8jAYuGNyNO8cP4c1r3kO68G6pgyA3w+RoZ+BYKnR+nhGFrCe0X/xdFFffl+NijLqxRODEGiGtN+m8gNr0kF7gZVvpbCToajRjVzB2/Y8a0f8P4j+iifnpp5+45ppruPrqq9m6dSvV1fbfl5KSEl588UUWLvTcS80VGk1ikpOT2b59O61bt3Zavnjx4gZ9lf4LaBkURcsg3zq+NhVdw5KYM3wKCzK3sibvANWalVJbFUfLc1iRs8fRbPL1ffN5tPt4xsbXlXXG+IcRc4IY+besHVg9lFTrSLYXpZIUGOWVwOwvyXSKRp0ICcxKXct17UY6edFYdBvTD/3BT2kbHM0wA1QTF7cayK0dznT2rbn5Zpg4sUEzxx/S1jErzS58rE3p6DW9lq6Ydj3d4kKZdgKBkVKyuySdVbn7seo2OobGs7ngqOeUkEFg69FQ26THN7gTN4CfYkCX0ivZOVKew5t75zPVTdquFgs/XoKiCnSt7hdLTzAS9FQWUqkjL/WJjNbeD62dH36L7SZ9tSXOM6Z+Rc/hXeh0Wp22pcpczdRRz5CxJxW1X8PeU/URvPwQJRd2RwaY6ohMDRFUSioJWt2wR1Z4tIargFP/5ONeojANMWtjDx48x713iSIEowc/yjUXdiLI307uA0L8UVTPZd5Gk+6YYy2Z+XlGNHOmW6hPDwuyjUx7LJG7X8lE0zihQkqxa1QCr0EIE9I0oibF4+5zYEP41UQHlcSa1gTu5qjYt/l/Aikt2K+L/3/GCfi/iueff57p06czadIkvv/+e8fyIUOG8PzzzzdpzEaTmPvvv5877riDqqoqpJRs3LiR7777jpdeeolPPvmkSZNohneEGAO4os0QrmgzhKd3/siO49sB+027tgy2UqvmiR2zCDUGemwUeNxchCoUj/4sNqmRX13mNYqxpfCoUx8lVyizVXGkLIcuNR4zmtSZsnUmG/IPO1ULVWoWvklZzeGybN7qd61zVOIEAqNJna+OrnR7TLNJZVPhEQ6WZtExNB6AYksFU7d+zY7iVFRhtxC0Sd2jmaADJ56eBMtY7+Llat3G3Z3G8e6BxV63nZ22AVNKOGd07UCP5DjHD3q5rYpiSwXhxiAyDmU5ERgA4wa7WLd+Gqn+GVXdEYM0CQeJcWyjCGa/OY/HvqtzcP3l3YUc3HIEdEngBvfkFEAtqyZ89g6qe8RT1TEG3d+IYrbgvzcHv73ZKFbnG7ZqUMjP8kMIhRNv0N0ScumWmMP+rBZoum9kZs7WLgztkMrpHewC2LqIjH18JexpOgT2cNpn+MWD+ePLPz2MKmnfo5LSQhWDUbJ/WyC/fhbNhj9CcaU4Wvx9LIER7bj0tmzCwmp6FSnRiMBrIOgGRyWQCL4VWbgS1xEZFYzdwGRvrSECL0OWbMA9dETg5R7W/29AVq9CVnwMlvX2BWoyBF0LAZcjfDDT/H+F/4gm5sCBAwwfPrzB8rCwMIqLi5s0ZqNJzE033URAQACPP/44ZrOZq666ioSEBN555x2uuKK5BO+vRoa5gIXHt7lcJ7GngmYcWuKRxIQYAxzExxNCDN6N43z1SXl5zy9c2WYIgQY/iizlrM8/5HI7iWR9/iGW5+xhTFwPl9sAHCvP82r/ryBYl3+QjqHx6FLnns1fcLDM3q26fuTF6znYJIYt5pr51VTnXBeFjPYeiTEIhUyzbw6VUki+3rGWLxdsoWfbeO6beDqzctawLHu3Y46GGzXEabGY5pVgOGCvSJIKbnUwUgXTvGKqL20ompW65M8f1jL+9k50G2YXe8/98DdHI0bFojmdsysEKCrKpnQCNqV7PLcegyxc9xh06m1w8mGphRDw6mW/ccsXF5JR5JownAhNV3lg1jguO203VwzcRWKEXWtjE/0xht+G8BvSYJ/Tzu5Nu95tSNmVhk0VVHeNo6pTDHqgEcVsxX9fDnt25nBZD990baqqcPWz7xIUGojUywELiPAaolbv/Ex9IfwdZPFUoBq76BbABsaeiIgP66IQ/uPAPKumyujEa1Xj1+LvvUz+34y6Hk/1rpN2DFn6DFRvgPC3GlzD/9f4j6ST4uLiOHz4MG3atHFavnr1atq2bdukMRtFYmw2G99++y1nnXUWV199NWazmfLycmJiYpp08GY0Hkuzd3uMfOjY0yW5VSUN0ki1GBPXgw8P/e72GAqCPpFtiPShTUKv8NY+EZl9pZk8ufMHr9vVHv+X9I0eSYzNBy2KEAJbjYfO+vxD7CvN9On4TrAzQ/zmFQOgJxqpvioS6xm+lZDrUhKomnwyE5QSdNV+09p9LJsb3/4BdWQRsp5izxatwvAQrKNC8fuuAP+ZhR6FvEIDdW8Vhp2uS+6llDx67id8uaeKwJjzyUtvqLVwSycEPDzzLrYv282v0xpGmhRF0H1YZ16ZE4pSNQPn0t2GiA6u5LXLF3Hft+fQr00W/kYbh3Mj2ZEe53YWmq7y3YZefLeuB1E7U/DbmUOLlpG8u64XIS6CiKqq8vJvj/PIJa+xKTEIPcSvpvpYoBtVzINaU9U5hrC5u1GqfPBoqrJyeFsKvUZ0Qyievy/C/yyIGQJV85C2A4A/wn8MGPs5pVGEMELkx8iyV8H8I2CpWWOCwEsRIQ/Zt/kfhbSlIcteqHlV/8Nb8zmvXgRVIyBgwt89tWb8xbj55pu55557+OyzzxBCcPz4cdatW8eUKVN44oknmjRmo0iMwWDg1ltvZd++fQAEBgYSGOjSGaoZfxHKrXZHXm+RlPzqUrckpmVQFOcl9mVB5rYG5m+1P6U3t3ctxD0RPcJb0TEkniPlOU0uNT4ROpL0GqGwK1RrVgqqSr2msTSp0zXMXpa7NHu3Q/zrK1ShIJE80nE8Ud+ZmLL7a/QEY6O8YXQkM4/51nNGCJDl9id0XZeIfA1p1cGI8zFrqqaqr4zCb3YRotILPTKCaYG7KhpBZYXCbx+/zSVPDMVgVLFZvRPEpE4J3Pr6JAae248h4weQ1CmBH1771UGCgsICOf/WsVzzcDBK5dSavTyNq1BoGczujAJ+uftbDKp0+Mek5IXzxJwx7M9q4X53IagqllAJx4/k8MOrv3LjS1e73DS8RRji4r6wP81ZzF1zjfWwAMqHtSP0jwNer4N9N98/D0IJhsArvcaZhAhAhD6FDL4frLvtC43dXXag/l+DrPwed80m7VCQFV8j/ksk5j8SiXn44YfRdZ3Ro0djNpsZPnw4fn5+TJkyhbvuuqtJYzY6nTRgwAC2bdvWQNjbjL8HrYKivfYaAnhq52w+HngL4SZnkmnTNYQQPNJtPCoKczM3O9JQOpIQYwBP9bjEY0l1fQgheLnPVUxe/xH5Fh/Kbn1EhCmowbJyaxUzDi/l5/QNWLz0elKEINY/nIHRdtGqWav2KYUGYBQqycEx9Itsy8WtBtpdfNtBZ20te0s9t0doKqQEKhQoMCIsNgLXpCBaV1NljHFPmjSJdWAQxhXlbm+KUgFUgZpicbOF/dgblwVx2YNzGHrxQFbNXu9R+Hrnezdwwe3jHDdvIQTj7zyb828by/HD2Q4/GpO/CT1/ArUaFbcImowIvJYWZa8xvu9aRE3kqfa0W0WVMOO6X7hmxiWkFrjxktHs1VBg9/GZ99HvXP/ClShKw5REWm4x6/d50PsoAmtyJFqQCbXC/XUD8Av0o0PfU9cH5kQIJQT83LfvcAepF0LlL0jbERCBCP+xYOz/7xDOWvfirdkktoN/12z+HfiPkBghBI899hhTp07l8OHDlJeX07VrV4KDG98cuRaNJjG33347DzzwABkZGS57H/Ts6d5MqRknj9Fx3Xl93zxHRY87ZJgLeHnPHF7uczWa1JmbsZnvjq3hWEUeAkGX0AQyKgtr9A51EY3BUR04LcqLE+sJSAqM4tNBt3HhyteaelpOEMA5Cc5l/OW2Km7e8BEp5ble01cKggDVxKt9rkapyau3DmrhU0oHwCo1Ph54i6NcvchSzrfH1nhNRwns0Zv6YmtfIGv6/mlbQ8EmCZ23B7WggsrzY+33fncaR1VQdW00xj/L7VGLE8et+T81pRrPGhOBzSqwVe0iqcMwNDceKoqq0K5XG86/7SyXN0NVVWnZqa5qRupmsO32cFwAFfRC+1/VLy75mqpI/I06Nw7bylO/jHb5HgZsTXdoeAAqis18s3Aj15w3qMG2u1OOe5kTIARXvn89B2euYdfKvS5JnVAE591yJgHBJ+fKfaohzT8jS58AbNRqTqT5S3u1VMSHCMV1hPZvgwjAa8n5/3C6rBnuccMNN/DOO+8QEhJC165dHcsrKiq46667+Oyzzxo9ZqNJTK149+6773YsE0KcVO+DZviOQIMfj3Qb71VfokmdFTl7yaos5t0DC1mavbte+a1kb70bcv2U0h/Zu7BKjZf7uA7Fu0JeVSn3bPmiMafhFqpQiPcP59zEvk7Lvzr6p08EBiA2IJzpA24iPqDuqf2CxP58fmSFz/MwKAoW3cab++YzJ32j16PG+Yczsc1QMiuLMCoqM1NW+kSYpASZY0LfHwSlBvwOZqPmV9jfK1+KpmKNmJ9LIPC5LKRFOvxganevbhOJLSYY1WzBdLgApbJhQ1FFlXTuY+b560tZu2C22+N2GdiBp+dMdRndcDM7p1cFOQYO7QhEUSVd+pkJCa/9rdCRVXPxpJlRhM64nkf4cOU5ZBVW1/UqsGoEbknHf4czMZEC3pq7hojoUM4bVPdjuXrOBj589SfoHe919h17J3PJuP5MGfU0afsyEYpA6tLRzLLfmJ7c8OJVHseostj4Y8sBft9ykPJKC8nxkVw8tAfd2sR5PX5TIKtXIUsfrrek3vW0bkMW3QGRM//RiIzwG42sXuphCxX8/reFy43Gf6Q66csvv+Tll18mJMQ5LVpZWclXX33195CYlBR3dtjN+LswLqE3h8qymJniWWuhI5l5dCVLs+1Pw77cVHUky3L2cLD0OB1DE7xuL6Xkga0zSTd7cyR1D1Fz15RIuoUl8ULvK51Ku6s0C98fW+tzJVRRdbkTgQFICIzgrk7jePfAIq/7G4SCQahM3fY1q3L3+XTU7KpiOoYlcFmb0zlUlsVXKe7LvwFUBF1LO7J9dT7SWkcK/PfXmaepeyqxnuFdA2HrG0TZF20w/V5G66MBtA6KpiTUj3VGGzLACLoERVAxKJmArRkE1DRltMOuPQkI1li7sLJ2UQMYTCrP/PogYVG+98QSShBS7UBpfgrvP5rAqvnh6Jr9yEaTztlXF3LzE8fxC+2PtG71Op4iNH59+jJ+/GU/HzzzPaLKhimtCHFClEQKsLSNAlVh2tw1nD2gM6qisHL2Op67/E30ACP0jHNt0lc7d6B1bAQRseFM2/QKy75dzR9fraAop4T4trGcc/MYTr+wP6q79tlATlEZk9+aTXpesUPHtvtYFr+s2c01Y/px74Rhp5xMyPJpuE/faWDdCNYdYOp9So/bKAScC+Xvgp6H2x5PQdf9/fP6B/H/3bG3tLQUKSVSSsrKyvD3r6t81TSNhQsXNrlAqNEkplkL8+9A51DfzK7W5h3w2L3ZFVSh8HvWTp9IzPaiY+xvStVPDSa3H02QwZ8KWzUllgoqdSvfpKxmTFx3eoS3wqLbuHPTZ1TpDSMI7uBOvDsxeRgLM7ZyuMKTy6p9/x3FqazM3deoc8muLIYICDV4Ty9IYHifZGSaPzuPZjnST0q5xUEwTMvLqLo+GgIUUN3f7FQEWpiBwXcM4cXeV/Lt0m28/fMqoKZbce2+Air7t0RYNQJ2HkdVJZoO976ayQ8fxHn8nGhWnaUzVzHh3nN9uxg1qOIapl78NWmH/B0EBsBqUZj3ZRTHjwXy/O9nIzQfUjz4oxrCuOKSIez4dAVbl+x0lIPXQtZUGlX2sQu6c4rK2XMsh26tYph2z+cgQTFbMR3Kw9KhhVsiI4FHP1vE1w9fhX+gH+fcNJpzbhrt83lLKbn3g185XmAXVNe+v1rNfGcu2ULr2AgmDLVX4Nk0nZW7jrJ6VwoWm43OLWM4b1BXwhuRqpJ6MXglgyqy+g/EP0hihAiAyC+RhTeAnkltk0n7nx8i/G2EsfM/Nr9mnHqEh4cjhEAIQceOHRusF0LwzDPPNGnsRpOYr776yuP6SZMmNWkizWgc+kS08VqdYxAKWVVFTdJ7ueuCXR+7itN4Zc+vTRjdLrztEBLPpLYjmJuxmU+PLEOXEqXmyfT71DX0j2xLx9AEdhV79iA5Ef5qw3z6zqI0ZqWuJaPSfdVTLQSCWzd83KhjQp0YOTYgnO5hLdlbkuH2/ZHAuKSeXH736Xy8cD0/rtxJeWU1eoDRkfIRVZKgZ45T8WyinY/UkhFNgiqI8Qsl2OBPq6BoLmx5GoOjO2Cx6nyy0JNRGlT2SyIiNZMBw0uYMDmP5G7BvDXFgKdYnVCE3QSvkVj8XQipBwJcdnSQumDz8kA2L97PgLMuQlZM8zCSCoETHKXFT81+gLvPfYFjqw7URNEFQkqkv5GyMR3Rouq0euVV1WxfvpvCrDq/nuDVRykN9sOWGAa6DoriiFjVYn96Luv2pTKkW5tGn/fWQ5kcyPDUWBW++H0TFw3pTlZhKbe/+zNpucWoir2dyKJN+3n/1zW8cP3ZjO7r3vPJCdJ7J3MQIL1/t/9qCEMytPgdqpcgq1eCtCKMPSDgIoTie7Tv/w3+nwt7ly9fjpSSUaNG8dNPPxEZGelYZzKZaN26NQkJ3h+aXaHRJOaee+5xem21WjGbzZhMJgIDA5tJzN+EaP9QzorvxW9ZO1zeKBUE5yb2ZfHx7VR7qeQ5EVJKEgMjPW7z5dE/mXbwtyb1ZhEIRsZ049HuF7E27yCv7p3rWFe/AePWwhS2FR5rVBQJcPSIqsXXKat498Ain0usfU1b1UeEKYh+kXVmTbd1HMtdm1zndwVwccsBxAaEA3DX+KHccu4gMgtKWZaQxKynf3R0WDbsqSLk1lQs54ZhHRKMNAk6hSVy+8BzGRjVvkE6YvPBVMqrPIu+pcnA1IUtGdo5DuF3G1Z1LEJc77Z1FIAWEUCeSeFgRh4dEqN9ToMs+nipx6upqAqLP1vGwHOnIoNuhooZLrZSQYlABN3qWBIQHMANH97MXc99g+lYIULT0SIDsbSKANVZs5MUHc6+jc5pcGHTCV2wB2urCLvbcJAJtbwaPdCELd5+E1UVhSVbDzaJxKzdewxVUdDc9GmSQEZeCWm5xdw1bQ5ZBTUtIepFlqw2jYc+WcAXD15Bd180NEoUiBCQDRts1kFDGHwkRX8xhDCC/9kI/7P/6ak04y9GbffqlJQUWrVqdUrTqI0mMUVFDd1HDx06xG233cbUqVNd7NGMvwoPdruQ45VF7ChOdURlav/bNzKZB7qch9lWzbKcPY32cDn3hOqg+tiYf5hpB+1NCH253YcaA/hs4G2O8uQ+EW0cN/BPDy9zm8bQm/B4IhD0iqhLeW4rTHHoYE6Vj40r3NXpbEcX6iqLjWO7y4nekExWQSnSpKG2rka0rkQxwaWtBnNPZ+cfbpPRQHJcJFfccy4rZ64k51iuoyJGybPh/0UBgTOLaNurNe+suQOTnxGrTcOm6/gbDXUtCipdN2w8EZWGW1EiOtmPDfQe1YMdK/Y06O5sjQuhYnAyWkwwy7Gy/IWvSY6L5P6LhzOku/fS4vzjhR7fQl3TyUnNB0AEPwBKJLJ8Osjimi0EmIYgQp8mvzyYX9asZ+3eVDRNp3e7BMJbRlEU4TrSoyiC3u0SaBUTTnZseIP1QoIptQhTat1vWsmFdU69UkrM1b6nMZ3OS0pHqbgnrNmTQkaeaw8fiT0w9NUfm3n15vO8jiWEERl4BVR8imtNjAD8wL9h089m/LMQnAJNzCmZyV+Lffv2kZ6e7uhWPW3aND7++GO6du3KtGnTiIhwY6HgAY0mMa7QoUMHXn75ZSZOnMj+/ftPxZDN8AFBBj8+HHATK3P3MT9zK7lVJcT6h3F+Un+GxXRGFQpXJw9zVCb5+h25o9NZRPu7D+l+e2y1XYfhw4gKgktbDaJVcDStgqOd1uVWlXCgzBcthO+QSC5uOdDxelbq2kab3DUW5yb05byaaqryympufXs2e9Nya665AKsBfZ+BkIwIPrj3IrrGuw+bBoUG8uafz/LKte+xbcmuuhUCBp3Xjymf3c62o8f5/PfNbNqfhgSSosO48ow+XDqiF61jffsRaHXCTf3yBy9k29JdTsuscSGUntetgU/NsZxC7v7gF9645QJG9mrn8TgRMWGUF7nvbq+oClEJ9jkLISDoBgicaBefyiowtEOoCWzYn8Z9H3yOxaY59CV703IcH+oTDSBVReBnNPDw5aMA6DumB2EtQinJc92qQgJ6sJ+9K3ctBCTHeY5IuhzLlkG36F+waUketwsPIobShgAArg5JREFUDmBXSjaKItB1198lTZes2HHEUf3pDSLoNmT1qhqflfqfebvuRIS/4tVZuBnN+KswdepUXnnlFQB27drF/fffzwMPPMDy5cu5//77+fzzzxs95ikhMWB38z1+/NTekJrhHQZFZVRcd0bFue710jUsiRd6X8GTO35wmOQJYX9S9FeMWKXmuMEnBkRyU/tRDcqbT8TWohSfCIwA+kS24bq2I12ur9Sa9pTrCRPbDKNnvUjM1sIUrwQm3BhIqbWySWmkUEMAJVYzkzfMoGVgFPkbBPvT7VoIx2jBNkRyJRUxViZv/Jjz2/fmklaDaBcSi5SSnSv3smDGH6TtyyQkIpgzrhzKs788RG5aPnvXHkAogp4juhKfHMtPq3bywrdLUWq0EwCZ+SW8/uMKNuxP5bXJ59MxqQWHM/NdetUIoH1iNJ1bOlcC9DuzF3dPu4n37vq0xipBp2JoW/uH5QTxa60nzUvfLWVYj2RUDyXXZ10/ik8f/aaBALcWuqYz9tqRjtd5JeX8vGoXK3cdpaLKQofEEi4cXMWDHy+g2mZzirjUv/H7GVWqrHVp034dEmkTF8Vd0+ZQUGImPDiAPlcNZus7vzWYQ+0oFae3cSZsEsaf7lsPJccuWhay8FKGty2hRciVFJQHuuzOLQRcMbI3BzLy3F6bWtg0HV1KVB9ITLXNnxIxg3C/rzFafqiJaAkwDUYE34Ywndao82nG34T/SIl1SkqKwx/mp59+4vzzz+fFF19k69atnHPOOU0as9EkZu7cuU6vpZRkZWXx/vvvM2RIw4ZrzfjnMTquB30jk5mfuZV9JZkYFZUhLTpxRmw3zDYLmZWF+ClG2gbH+Pa050PgMkA1clens7kwqT9GxfXHLNY/jADV5NW4z1fc3elsrm4z1HmuPpyPn2IEGi92VBCU2ipZm3cAHcnO3HQsWyOcfkxEYhVKX7tGQShgQWNO+kbmpG/k8e4TOPDMRhZ/ugzVoKDZdIQQbF++m+9fnsMbK55h3A2jHGNlF5bx0nfLAOcbeO2/Vu5K4dd1e3hy4hhueP0HLLaGnisSuHpUnwbXpdpqI/aMLtw5/0FSft/F4ZQc1kS572AugbySCjbsT+P0rm3cbnfeLWNYMOMPctPyGhjGKapC54EdOP1C+411y8EM7po2h2qLzXFOabnFLN3mullofdQnMELAlkOZbDqQ4Rgnv7SCpYog8tzuhGxIpTS/TjciA01UDEnGmhxln1dNVOeBS0YQF9k4m39Z/j7oxRhUjdcvX8xtX11AtdWAVkNkFKGjS4VBnVtz/Vmn8enijfxZE2lxhdpSb09EEezl3B8tWM+CDfuw2jSEUBnR4xFuO68r7RNbIpTm9jD/avw/F/bWwmQyYTbbm+kuWbLEoaGNjIyktNRzQ193aDSJGT9+vNNrIQQtWrRg1KhRvPHGG02aRDP+WkgpyTAX0sIvlHaJsfSPaoephliEmQyEmRr3A9cvsi3r8g+6jXAIBDe0G8UlrRq6pdaHv2rkgqR+/Ji63mUUpMbU3idhr4KgxGJucHMeFN2B37N2up2rgiApMIqcane9hRrC7syrotU0oaydu61YcX4aCrbZCYxwfsCv3f65XT8RtCQVFfvTttY7AC3JhKjUydpawFPjX2Haplcc5zRnjXO6xxW+W7aVWY9PIiEqlGM5DfVr9gjKMnq1S6R1bAS6Lvnyj0188dtmyurpaTqdlgTp7qtrapFd6ElECkFhQby16jlevfY9ttZLjwlFMPzSwdz30S1U2zQOHMvmzvfnUG1tnAjdFaR0FojXQtclRa3CaDV8LA/36ERhdjGhsWFsrTIze80uLBX26p7OrWK4cdwAzujdOOdqKaug8ldqvU+6JeYx67ZZzNrYg8W7OmC2GGkVVcJlQ5M4d8SFGFWVi4Z055NFG9zegCRw5Rnu9WkAxwtKufbV7ygur3QIg6WEVbtTWbM3nQ/unkC/Ds0kphn/PIYOHcr999/PkCFD2LhxI7NmzQLg4MGDJCV5Tr+6Q6NJjO5Gbf93Ydq0abz22mtkZ2fTq1cv3nvvPQYMGPCPzunfjO1Fx3hh9xxSK+puSKGGAK5uMxRFKORWlxBhCmJcQm+SAqN8GvPKNkNYneda+yQQmBQDFyT182msye3HsDH/CKkVeQ2IjKz3/96gI9ledKzB8stbn87i49vdzNWOLUVHfTpGLXqGt2ZHsYveOycEfZRk+03RXTBI6hLL+eEYl5difiAOGVdnTFdpleycW8zONfvoNdQefj3kJkVUHynZRazcecQlgQH71ay2adzw+iy+eeRqvlm2lW+WNvQWOeShPLg+InzwMYmKj+CV358k/UAm+9Yfwmyx0n5QB2JbRvPm3DXMX7/XZdTor4CmS7YezSJq4pmcNs5ODgYDky8YTEGJGT+jSkRIE2/4ehF1HaftiA8v596x67h37LqaJQYIvBylxiQvNiKEx64czXPfLEFVhIOE1H5khnRP5qKh7ru5A7z2w3InAlP/XHWp8cTni5n//I0oHsz9mvEP4z8SiXn//fe5/fbbmT17Nh9++CGJiXa/s0WLFjFu3LgmjdloEvPss88yZcqUBt2rKysree2113jyySebNBFfMGvWLO6//36mT5/OwIEDefvttznrrLM4cOBAk93+/j9jd3E6d2z8tEEUotRWyYeH/3Dq9TPj8FIuaTmQB7qejyo8h65Pi2rHPZ3O5p0TypYVBEZF5fW+E4kw+SYeDDEG8OmgW/no8BJ+TF3XJF1KLXYWpzHj0BJuaj/K0TOpa1gSj3afwIu756AI4TRXUe+1J5yX0JcBUe1BQPfwlqzI2cuu4rQGcxXhVlAl1Ji6iRYWPF5KVWA5LRDLmJA6D5jaG41RYLkonHePLObTGhLjZ/Tt6zpnzW6PYlGAovJKrn/9e3KKXDft9CLTACDI38TpjSg/Pmg280VmJvvTc2Fr48Tmpxp7U3NoG19H2o2q2ujUUQOIELw2u0SCCHdactHQHiREh/HFb5vYWCPWTogO48ozenPZiN4YVPcforziclbuOuq2PF5KyC4qY/3+VI9pv2b8s/j/7thbi1atWjF//vwGy996660mj9loEvPMM89w6623NiAxZrOZZ5555i8lMW+++SY333wz119/PQDTp09nwYIFfPbZZzz88MNe9v7v4b0Di9Ck7tFwrX5H7NnpGwg0+HFnJ++M+OrkYfSNbMuPaevYWZSGUVEZFtOZCS0HEldTPu0rgo3+mG3VnOwtTSL55MgyNKlzW8exjuUXJvWnZ3grfkrbwNbCFFRFYXB0RzbkH+JA6XGPxOnMuJ482fMSp2Wa1O3RlRN2EwYQyZXIw7UN7nxAiGonMK4ceRXBrph8Ms2FJAZGMqRba37bfMDjcAI4llPkkcDUIqeoHCHw6A/jCVJKZv6xhWvO7OeSYJVXVrPxQDqV1VYOZuQxc8kWh5kh/LMPjsZ67QIsVhtCEU7LmgKhBCP9RkH1ctx3adYQAQ1LnAd2bsXAzq3sZfOajr/J4JOeKzW3yOv7pwhBSlZhM4lpxj+OtLQ0j+tbtWrV6DEbTWLclfrt2LHDyYXvVMNisbBlyxYeeeQRxzJFURgzZgzr1q1zuU91dTXV1XV5/qYKh/4pWHQbh8uy0aRO2+BYp35C3pBdWcw2F+kVb/gudQ2T2o4g1Og9TdAlLJEne1zidbsKWzXLs3eTXVVMuCmIUbHdifSri9SUWitZfHy7x+fXxuCrlJX8H3tnHR7F2fXhe2Z3425EiBA8EIIEd6cUp5QapbSl7t6vb93lbftSo04daXGnuLsTgoQYcfdkd2e+PzZZsmQtgs99XduSmWeeeWaz2Tlz5Hduj+iLl8MF1dYWbgE8F2V685ibtN2m52dz1gmjEVFDlGdzszkXAGL7UvQlKshwhBwHZNdyy94YvQyOguV4E4Yb0NxTO6k84cS/B2wnuAqCgKNGZRKasIY9BoyHiyNFZXX1Z8oqtcxevpOdcUl8/cQkoyGjlyRmL9vJ7+sP1MlxqU9370uFShTp1ro5/2w9wp8bDnIuw6DiHNsmlOkjYhskbleD4PaYQYHWULR98V5wmoigjjRzpAGNWoVGbb8x5exgu9uzJMu4OF7/XaF1eolNh8+wePsx0vOK8fN0ZWzvKEZ0bYODnV7MK8YNEk6KiIiwapw3pIG03b9Zb29vk94HtRei1+spKSnhoYcesjJD48jJyUGv19OsWTOT7c2aNbOoTfP+++83uB/DlUQvS8xJ2MRfiduN8v+OoiEJ9tE2I3Gxw5jJrbSecGkJraRnR3Y8o4I7N+j4i1mcspdP45ZTIWkNoStZ4r9xy7m7xQAebD0MURA5V5KFVm66nAhJllifcYzJYT2tjtMIaiqwXuZdJel4fO9PzO//NGpRRXZhCUf356A56UWlYwVCSCWCw4VvD0EETY9i+hFBebrAPuGw4cvl4r/bmm02nrYFBObtOIh83M0uo0SSZTLyiu0aaw8atYpfX7ydiW/8YraCRpJlDp1NY96mQ9w9PBaAj+dvYv7mw01y/otOZrVpoz2IgsDYXu35dOFmVu+NN/m1HDidyr5TKTw/ZRC3D7GeTGsJQRMFPnOQC54DKY2a8JIsqziTP5wj2VPo1ibXJJTVGNqFBRDg5UZWgfmwIBg0c/pHWzacrgfKq7Q8+dVi9p1KNVaXJWXmszc+hT/XH2T2k5PxcHWyPdGV4gYxYg4ePGjys1ar5eDBg3z66ae8++67DZrTbiPm888/R5Zl7r33Xt588008PT2N+xwcHIiIiKB3794NWsSl4uWXX+aZZ54x/lxUVERoaOgVXJFtZFnmraN/s+qiZNRKScs/ybuJKzzPNz3ux7FWf6DzZXn8m3GUQm0Zwc7ejAyKwdex4fH9pip5XpN2mPeOLzL+XJN/opclfk7YhEoQeaD1MDRi49z4FyMKIgVaywJrNQwIaM/q9EM282JSy/PYkhnHsV35/LpuP5KsB0cRcIFjrohRZYgtL5RoR3mF8E73STirHVid1pY3jiwwyb9RCSK6ctAfdEHdoxjUVp5MZAmpzHp+y8WUVWrRqES0euvXdbFA3MWoRIHR3duxZl+81fwVWZb5a+NBpg3rRkp2QdMaMLIMgoDD6Wx0Pi5IPi42Db/qA6ltOYqCwQbqHRVOTMsQ3vxtrXFUDTXvxScLNtGnQ4TdwoEXIzjEgv8GCvK3cDYlji3Hklm+343CchcQtiDLW+jZLox3Z9yEj0fjqoZUosiDN/fi7T/+Nb8WASb164Sfp6vZ/dcasiyD/ixIhaAKRVAZciE//XszB04bGtHW/B5r/n/qfDZv/r6W/z447sosWsFITExMnW2xsbEEBwfz8ccfM2nSpHrPabcRM336dABatGhBnz590Ggur3vSz88PlUpFZqZpB+LMzEwCA833FXF0dMTR0f4QjD2cLc7kdHE6DqKa7r4t6/TpaSwH88/VMWBqkJA5VpjCsvP7uSWsFzpJz8cnlrI4dS8CAqIgoJMlPjqxFEdRjUu1Bkt9DfQIV/9GX4ckS8bWBJb49dwW7ojoRxv3ILw0LhRoyxp9XjDc+IOcbN+Abo/oy6q0gzbHqQSRnw5u48TaMoTQSlStyxDcDZ4juUxEOuuMlOSIGF5Jb782fNTlTqOROSq4M208gvg7eRe7c86gLZWRjriTds5wrVK61uDNsRBykmWQUurxBFl909fqJSIDfUjIsNzwUpJlYts0Z//p1DphJVEUcHLQMGNUD75eut3maTPzSxj18ve0CPQxGgxNQnXSjt7LGXVOKVU+1m/6oiDx5oQNtA3K5n9r+7D9jEH4UJahf8cWPDmxP2/+ttaqASeIAgu3HeXpyQMatOT0vCL+t3Ar6w+ervaI1So6qD7lvlMpzPxsAX+8fCdODo0LdUzsF01BaTlfLd1h6NItCsiyjF6SGRnbljbN/fhi8TY8XJwY3q0Nwb7XZoNFuWINcvFnoK+pJhSQHQdRqn6GpTuOW/x9SpLMpkNnScstumqv/UZJ7LVE27Zt2bt3b4OOrfdfT00jJ4CKigqqqkyf2j08Ls2HxMHBgW7durF+/XqjVo0kSaxfv57HHnvskpyzNkmlObx19G+OFlxITNKIKqaE9uKxtqOMfXMay+KUvVZl8gVgYfIebgnrxecnV7I4dW+1J1I2+SOub9NHMFTshLj40Nk7omGLr8XJojQyKgqsjqmSdGzNjuOm4C7cHTnQ2OPoYgRALajsDjk5qxwY3KyDzXFtPIJ4q9Ot/OfIPKvjZFnmdFoWYpSA2Lrc9IbvLCF2LEXO0CDLcLwgpU7MN9KtGS9EjSensJQ73/+DvOILxpoU54qqWRWyWjYxZKptEeSTrlBlvVrMhFrnTs0poHdUOLvikuoYKQIwvFsb3rpnJP9buJUFW46gq+W5iQz05Z0ZowgL8MLZUWM2kflisgtLyS607QGrN4KA3t8NpyNpVLW1XIWoEiUmdTvOTZ1Oo9dDdGgG28+EYdAagq3HzrH12DmbRpYkyYYKqgaQnlfE3R/8RUFp3ZLn2uglmXMZeazed7LeqsDmmDGyB2N7dWDF7jjScovwcnPCQaPmx1W7Wb03HrVKRJJkZi3eysS+0bx42+BGJzJfTuSyhchFL2Eal5WhcguO5Xtp5jGO1HxPS4cjA/tPpRDc2/b3whXhBlHsvTgvtUYs94033qB164Y1Jq23EVNWVsYLL7zA/Pnzyc3NrbO/IYk59vLMM88wffp0YmNj6dGjB59//jmlpaXGaqVLRWZFITN3zaZYZ6rqqpX0/JW0ndyqEt6Omdok50oty7Ma3pCBtPJ8ciqL+Tt5V4PCoOZE5FSCgFpQ8UanKWYTr0p0FcQXpiEI0NYjxGaScbHWtgKugECx1qClckdEX9LL81mQvMtoxNX8v4VbAAkl9t9Unms/Fme1g11jhwd14stTa6waXBIyeiRUrQ0Ge+23p+bfQpAWuVikyL2ccyVZtPWo2x/pp9V7yCsuM725lavQb/FG7FSMEHAhP8dVcCI0P4S4M8U0NNhdpZN4797R/PbvfuZvPmxsDunp6sSdQ7pyz8juqFUiz986mJmje7HzRBKVWh0tg33pGBFo/BwM7tyKJTuON2gNTYk6pxR1WiG6QI86uTGiIOGs0XJXb0Moa/HBKGZvNJ8TZctLJAi2y9kLSytYsfsEZ87n4uyoZnDnVnRr3ZwvF2+3acDUPs/ynScsGjHFZRUs2XmCVXviKCqrILyZD7f078SA6Eizmi9+nq5MH2HIS9p46AzPfrvMuK+2gbpo21FUosDLtw+1ucarAVkqRS6uyW28+H3VIwrlPD5sFy8uGGl9nkuyuibiBsmJ8fLyqnN/kWWZ0NBQ5s6d26A5623EPP/882zcuJFvvvmGadOm8dVXX3H+/Hm+/fZbPvjggwYtwl6mTp1KdnY2r732GhkZGXTu3JnVq1fXSfZtan5L2EKxrsJsRYoMrEk/zJ0R/WjnGdLoc3k5uBg7UVvCU+PMlswTdinZ1uCqcuSFDuPwdnAjyMmLnxM2sTb9MDpZQkSgv397Hmg9jFbupqG5Cn0VX8SvZknqPqqqvTtOooaJoT14pM0Ik9yc2tSu5rGEjEzz6nGiIPJ81DjGNY9laeo+0srz8dS4MDIohl/Pbbb5ngA0d/bhkbYjGRZoXRysNoIgcGdEPz49udzq7Krm1vOEZBlwrFFLrTuTTi+xZMdx8ze3UhXSTi8kZz3BYa7cO7wn46M6s2zHCd6S11k/qY38kDPnc3hsfF9mju5JYkYegiDQItDHpALmfE4hf289wp6TBi9jj3Zh+Li7EOJneLLt3DIYJwc1FVWNV9NtMDo9qpJK3FefpGRwK7QtfKvzog0y/iHeRbx/yzpCvIvRSwLfb44FZLxdyrm911HGdj6Jl0sFOcUuLDoQxfw9HSmpNG+IyzJWG1uu3BPHW7+tQ6vXG/WI/tp4iKjwAOJTsu1OqpZlOHM6jU9nzqbzkI70n9wTTXW1UUp2ATM/XUB2YYnRi5aeV8yO44kM7dKK9++72aJ+jCzLfLV0u8XyeRn4Z+tR7rupJwFe10AzyIo1IFt+KBIFPYPancPTuZzCcsvh/c4tLTdeVbg8bNy40eRnURTx9/enVatWqNUNC6vW+6hly5bx66+/MmjQIGbMmEH//v1p1aoV4eHh/PHHH9x5550NWoi9PPbYY5clfFSDLMssO7/fqndEJYisTDvYJEbMqODObMu2rAUiInBzSFdKdJUIgmCx58rFlOorae8RQoSbwR3/RqcpvBg1ngJtKe4aZ9zUdfMudJKeJ/fN4XB+kokBUSFpmZu0g4SSTD6PvcesOF5zF1+6eEdwuCDJYiNCX0d3evqZuhDbegTzfNSFBDxJlnh6/y9WDRgB6O7bii9iZ9ilrXExk8N6sjfvLFuy4hDsbHNQZw0CyBoZN7UTLdzqhjxKyispr7JeCaWqVNPNJ5JJHQwNOEd2b8usxdsoKq0wH++341p11Qrbjho1bUPrrmv9wdO8/MNKJGRj8nB8ajZ//HuA9+67iWFd2/DhvI1UNrEBUy+hO0nGMT4bQSchAB5r49F7OjH01W4Eh++mXVA23VucNzpn4tL9ySlxJcSriB/uXYSPazkq0XC2QM8SHhy0l5tj4rn/pwnkl5nm2KhEAR93F27q3s7sUvbGp/DqnNVG46D298LJ5Kz65QJJMhWp+axdd4pVP67n2+e8eX/VK7SIDuPZ2UvJLSo12/Byw8EzzFmzl/tHm/c0JWcVkJBuORcKDA8QGw6e5jYbLQ2uBmR9CoZbleXPoEqUCfIuNWvEqESBHu3CCAtoWKL25eBGyYmpnY7SVNTbiMnLyyMy0lCu5+HhQV6e4Y+lX79+PPzww027uquASklns1pHkmVyGljSfDGDm3WgtXsQCSWZdQwnlSDioXFmclhPjhak1FtzQyWYxsCd1Q5Wwy7rMo5Y1JqRkdmde4bNmScsdtB+Pmoc9+2aTaWkNVlrzQ0swtWf/51cycigGKI8m1s0QGwbFQJuaqcGGTBg6AT+YZc7WZa6n/nJO0kozmyYcnCVgLzFlxl75jOsa2sm9u1olLB3dXJALYpGo8LSdXi5XvgSdnbQ8PUTk3j4f/9QVFZx4YZmZ6mxWiXStrnlJO2kzHxe/mElesn0aiVJRkbm5R9X4uPhwpq98U3qqRYECPbxJC230PC7ddMbPhClKurUoksyYnEFLntNRbKcdDKxPcIZ3GI2apVEaaWGnWdDKavUUK41fK29NfFfvF0uGDA151YJMiFeRbx08zZe+nsEAgKCYMhT8fN04+snJuFsQVflx9V7LD481DuZWRRwjMtEX91yoSCrkOeHvsmTq17iTFrdUH0NMvDnxoNMHxlrNq+lpLyupk/dU4uUlDdNFeKlRhC9kO1QkfLzDIa0KqMHquaT1NzPi7emWw81XXGu43DSxU2jrTFuXP0ryOptxERGRnLu3DnCwsJo164d8+fPp0ePHixbtgwvL696L+Bqx1FU46pypFRv+YtBFAT8HJsmoVkjqvmq+7385/Bc9uSeNXzBYsjLiHD154POd+Dr6E4//7Z4O7hSUFVq12e3mZMnIS71exJZnLLXahhHRGBRyl56+LViUcoelqTsI7eqGF8Hd8aHxjIxtAc/9XqYL+JXsTPnlHEWufrYA3nnOJifyNykHfQPaM+7MbfhdFF4ShREojybE1d43ophIRPjHV6va7sYlSAyIbQ7E0K7szHzOC8e/KNex8sSyMlOFBZUUViQRXxqNr+u28e3T91C29AANGoVw7u1Ye3+eIvhBr0kcXPP9ibb2oUGsOTNGSzddYKNB09z5kgS2sQcHI9nUDqgJboAd7MGjQCM7tEeLyu9jeZvPoxswUSs2fbTqr1NLlAny9C3YwQLTu1B1a4UwaO6kWaOGumoGxSpDVdQpcPxZBYuB1IRK02fwgu7NeflPxLxcb2bPq2SWHe8JZW6C5+dAPdiCsudkGTzPh+1SmZI+wReuS2a/We0qFUifaIiGNKllUWxufJKrTHk1hRvgialAE3SBY+JpJcozi/hn4U7bIoVFpSUk5JVYFZvJtjXw2b5vF6SCA3watQlXDacRkLx+1YGiKDpxMcPP8jK3XEs3HaUjPxi/DxcGd+nA+N6d8DFyb4cOYWm5+Km0ZYQBOHSit3VMGPGDA4fPszAgQN56aWXGDt2LF9++SVarZZPP/203gu42hEEgbHNY1mQvNNiSEkvS4wJ6dpk5/RycOXL7vdxtjiDPbln0csSHb1CifEKN3ob1KKK16Nv4dkDvyHLsk3PwV0R/Y3xe3s5X55vdV4JmZSyHKbv+IrUsjzj7bBUV8mX8WtYnLKX73o+wOex95BTWcxfidv57dwW47GAMWi/Pesk7x9bxJsxt9Y5zx0R/XjlsPmkLwEBR5W6Sd//9PJ8u3JwapAlQC8gnbtgMMiyTElFFY99uYgV79yHg0bNfTf1YOPhM8hafZ0bjAAM69aGNmY8Jx6uTtw1tCt3De1KRVklcz9YxLK4tQjrT1M4viOyq0Pd0JJgWENKdgGh/l5m173zRKLNCpr41IZV6VhCFAXahvgQ0fk0Kq8ik3CJ6KdDGFhgKFvf5oUmoQDXvckIugt/d5KjmrKe4VS2N+TB5ZU6s/xwWy724GQVu/PM3NF4OFXwxPBdTOgaV2ctgiAzsYeKSQOG2bX2Cjs6bNcYD1bDZVo9TicycNmTXCcEIEsyiSdSIKThOk/e7i4M6tySzYfPmv39CoCbiyODreT9XE0IqkBkl7ug7DfqvqvVZQpuT+GoUTOxX7TNhplXJU0QTrpaPTGXuml0/e5qwNNPP80TTzwBwLBhwzh58iR//vknBw8e5Mknn2zyBV4NTGvRH0+Ni9ncDwEYE9KVNh5BTX7elu6B3B7Rl7ta9Kezd1255j7+bfmmx/108Wlh9via9U4K7cGt4fUXIvR2sC2QVawt53wtA6YGGZm08nzePbbIOJc1TRYJmdXph1l9/hCzTq7i07gVrE47RJWkY1hgNFPD+hiuqdbNSiWIqEWRj7rc1aR6PR4aF7sMGLFmLVoB/U5PKDd9gpckmdyiMtYfPANAZJAv3z09xaxWhQwcPJ3KzhNmumPXwsnFkXveuo356d8z9/jnLHxnBjNG9UAtmn42ZdmQgHr7u79bLBe2x8NSVFZhc4y9iILA8E56Pp46i6+SEgC5ru0lguAiIUaVgiAg6CR0Pi6UDGxJ0ch25E+LNRowtY6yvP4KJ95ZNohF+9ubHyDY/4Tu6eJks2u3jMzYXlG4OhuShtUqEVEwhKv6d2xByPZEfH7di+uuJAQLBqRTTpnN5GAvN2ernpRnJg/Aw8UJVZ0qLoPq+uvTRlz9Uvy1ENxfBpd7gJqQY/XfmuCJ4PUFgmOfK7e4pkBuotdVyoYNG4iKijLb+qewsJAOHTqwdevWBs3dqE9xRUUF4eHhhIc3zpV/tePv5MGPvR7i3WML2ZeXYNzuJGq4LaIPD7Sy70nuUtDZO4JvetxPbmUxOZVFHMlPZlPWCUq0FUS4+TMxtIeJB6c+jAnuyqmiNKt/G8U6yzc5vSyxLfskaWX5FGhLbeYNyci8dnQ+KsFgHuiqWxR83PUunml/M3382zAvaQdxRedxENUMbtaBKWG9CXU1daknlmTxd3WzR1EQ6OXXmkmhPQm2M5w2MKA9GlGFVrLs2nRXOzHEvyMLl8UjpzuCZP79VYki+06ncFMPQ6Jox4hA+ke34K+Nh+qMzS0u44mvFvPDM1OIaRlMQUk5S3ceZ9Phs1Tp9HQID2TKgE60CvFDpVYREOoHQOaGg2aDQnpJpqJKx4vfr2Dxm/fU+Qx0a92clKwCq79frU6ibXN/zqTlNLiVQTNvN56bMohov7/w0/zBopwIqmQRS8aHIALNK3E6k40sQEVUIJVt/EGs9zOXkVn/9uLmmHgc1BeeCvWSE2pNZ8DgtYpLziSnqAx/T1fahQbUeb9EUeCWAZ34cdUeiwagRqXimVsG8n93DGXDwTMkZ+Xj6uzI0C6tCPLx4P9WHGd/XIbFDA+VWqRzZCBysDfnMvLMe1IEuGNwF6s6L8G+nvz20h18uXgb6w6cRl/9NBzdIpCHx/ahR7v6N9q7kgiCCsHjZWTXmVD5L0hFoA4Hx8EI9TBEFa4Mn3/+OTNnzjSrI+fp6cmDDz7Ip59+Sv/+/es9d72NGL1ez3vvvcfs2bPJzMzk1KlTREZG8uqrrxIREcF9991X70VcC4S4+PB1j/tJLs3hTHEGDqKarj4t7OpjdDnwdXTH19Gdth4hTGmA18UcY5p348ezGxqtpHu8MAW/erRBqB22K9KW8eieH4l0a0ZaeR7OakduDu7KreG9zXbLXnH+AG8f/cdE5v9McQZ/JW7ngy530D/AwhN5Ldw1zsyIHMR3Z9ZbHPNa9C109WjJP+ete05ANnlCyswvZt4m87L8smww5L5auoOnJvXj4VkLKSmvNIZc4lOyWLDlMM/cMpC7hhrCZwUl5azZZznPRqoOKe07lUr3tqYtN8b3iWLR9mM21g9avURogDdJGXkNetirqNIxJNoROecPQCa1yg0VMjorHhRBBFFbBoKAztfeVgOWKa5wYseZMAa1SwQMSbirjscyLtiV7cfO8fGCTSRnFRjHtwj04flbB9GrvekD2j0jurPjRCJxSVkmhkyNSu5r04bjWd2jp8Zwrc34R0exd/Uhi+tUa7RMejSWaS1acv9ny0xKrGvyZAZ3bsU9I7vbvOZgXw/eu280L99eQVZBCe4uTtdGSbUVBJUfuNx2pZfR9FzHib1gaBD94YcfWtw/YsQIPvnkkwbNXe9Hm3fffZc5c+bw0Ucf4eBwwQLu2LEjP/zwQ4MWcS0R5urHkMCO9Atod9UYMJcKV7UjnprG9XYBUAsiEW7+ZsNxtpABrawnvjiNYl0FWRWF/Jm0jdu2/Y/jBSkmY08VpfHW0X8M4nS1DCEJGa2s56WDf5Jenm/Xee9rOYQHWg1FU13RJVbfRN3UTrzV6VYGNovCzcmByCAfq/dXvSTTpfWF0vu1+09ZPa8ky+w7lcJDn/9DSVmlSc5IjaHy6d+b2RVnMJ4MT+u2eyTF1wopybLM/M2HefJr+6oGMvKK+P3F23n+1kG0CvatE6KwhgD4ebhCxQpqPC8eqiq7OpYLZRJFo9ujD2yapPm8Umd0esMa/j3ekrcXdeDfA6d54uvFpGQXmIxNzMzjsS8W1QnvOTtq+O6pKdw/uqdJaKl7m1BmP3kLo3tYN5J7jO7K6JkGkbnanp7wtpX83zeJLD59nNbhMwiWhrDk2Xj+M7UdbUP9Cfb1oGe7MP774Fg+un+MRY0Yc7i7ONEy2O+aN2CuZ2pKrBv7ulrJzMy02qpIrVaTnZ3doLnr7Yn59ddf+e677xg6dKhJ1+qYmBiL3aQVrl3y7WikaA0Rgc4+EXg7uDE0sCPrM47ZbLhoC0mWqdBX8eyB31g26AU0ouFjPDdpB6IgmBUlBIOHZ2HyHh5ta7vc8nBCGsfWllB5yhu5WSUePo4MaNmKZwaMwM3B8KQtCAJ3DevGW7+ZF6QTBQEPVyeGd21j3FZQUo4oCkh66984JRXWy1+f/245L902hDA7Kkwk2TTY9N2KXXy7YpfN42pw1KhxcXJgysAY1h04Ve+w0sR+HZGlVdR0dB7qlco36Vak9vUyqhPlVLQMRhfkadjWSE8MQH6ZEyuPtGXJwXYcTgkEBD5ZsOliZxlQnW8uyHwwd0OdUJyzo4aHxvRm5uieFJVV4qhW2V39IggCT81+kLbdW/PPZ8tIjjtPq+gyPl2SgMZBRjSWg+vQ6FYwvt1mJvSdh6C+vrtQK1zfhISEcOzYMVq1amV2/5EjRwgKalheab0fjc+fP292IZIkodVaF/NSuPbwcWjc01sn7zC8q+d4qt3NBDh5XEiIbQQSMnlVJWzKPGHctivntFUDSUJmV+5pm3Ov2nOS+/47n+3HzyFVCsjJTuQfEljyz1le/m4V2lplgON7d+CW/p0ATDwUoiDg7Khh1qMTTCTsg3zc0dvoLm0PpRVVvDpnNb+s24ePu+2k5gWbD1NYWkFmfjHfrbTfgAEY2sUgSLjlSAIHz6TZfZxKFAgP9GFCn44IqkCo9r80dyxljE9idUeji6g2kBz/zKOyQ6BtPZx6lH9/s6EXby0dzOGUIARBIMjHnayCEsuF+7JBOffIuXSz+1WiiLebc73LdwVBYPT9Q/nh2Gcszp/DF2slHJ1kRNHw/hSWO/LPvii+3diVv/eEkp/+po0ZFRSubkaPHs2rr75KRUXdHMry8nJef/11xowZ06C5623EREVFmc0i/vvvv+nS5epXf1SoH2Obd6tWqmkYt4ZdyM/xc3Tnl96PckdEP9yqQ3FqQUW4i1+DDBsRgSP5F9z99vRrsqVwnFdUxuu/rkGWMfE41ISsdxxP5O/NR4zbBUHg5duH8OXjE+nXsQWB3u5EBPpw3009WPj6dDpGmLZxGNGtLWoLOiQNYcPBM2a1Qi4mPa+IzxduYdmuE/X6fapEkYfHGn6Hi3fYzp+pwVCNE8mPz0wx3OSdxlE7kfel0ANM8E1AREZARlVt4AglEi7vpCOkCsi2ujvb0XbB2qHREfY9+WXll5j8XFGlIyW7gNyixnkpBUHAxfk0onwWkJBl+HV7Z0b9dzofrBjAz9u68OHKfoz6IJofVqxBkiSTHkgK1xHXeXXSf/7zH/Ly8mjTpg0fffQRS5YsYcmSJXz44Ye0bduWvLw8XnnllQbNXe9w0muvvcb06dM5f/48kiSxcOFC4uPj+fXXX1m+fHmDFqFw9TKxeQ8WJe8hvaKg3mEgd7UzA5pFmWzzcnDliXY38VjbkZTpqnBSaUgszeaO7bPqvTYJmQ2Zx3m4zQiKtOV2de7ubEMUb+mu40Z5d0v8tfEgtw+5YLALgkCfqAj6REXYPL+HqxNPTx7AR/M22hxrL/tOpdoco5dkVu6OY1T3dnaFs8DgSfnq8YlG1eGUWomv1pjQpwP3j+5lUkouqPzB7Unkkk8pqXBAkuGF5geZ0ewkmwqbU6x1ZsU7TpQsr0LQyeh87MjFamSIad1B6/lJNfhUX39BSTmzl+9k6c7jxj5S0S0CeeDm3vTtENGwRejOGP/5974OzPr3gtGvk1TG/3+9/ASzV8QhyTLN/Ty5bXBnpgyIsSjMp3Btcb23HWjWrBk7duzg4Ycf5uWXXzY+TAqCwMiRI/nqq68a3AOx3kbM+PHjWbZsGW+99Raurq689tprdO3alWXLljF8+PAGLULh6sVN48R3PR/gzaN/szv3jO0DavFY25E4iOY/YqIg4qYx5Ja0cg/kvpaD+fHsxvr11AFyKov44PhiYn3tE+7q4Blqdf+pFOvJZTKQmlNIpVZnDBNJkszOE4ks2XGc9Lwi/DzdGNs7igHRkWYTMG8b1Bl3Z0e+Xrqd9LymaVdhD1q9hCzLdvXbGtsriicn9sPH44JWkLe7C4mZthOjW4f419HCkWWZlccH8ttaLafSDAZAqE8Bd/Y+zC29vJBc/sOqze8g6AzK2JKTulGeFnuwJxIV4OVG51aGcvfpH80lLbfQxEN3PDGTx79cxFvTRzKmV5SVmSwgGEKBWr3I7I09rA6tqYY6n1PIf//ezOYjCXzx6IRrSu9F4cYlPDyclStXkp+fz5kzZ5BlmdatW+Pt3bieVnZ/+hMSEmjRogWCINC/f3/WrbPSXVfhusLPyYN7IgfZbcS4qZ14rM1IJoZa/1KuzYOthxPm6secs5s5V2q/SqwMrE0/QqSbfVa8t6P1HB+NRmW4b1q5wQmCIcwCUKnV8dy3y9h+PNFYAisKWWw+cpbOLYP5+IExbDx0hiU7jpNdWEozb3cm9u3ITT3acVP3dvz3701mNWMuFf2jI1m+u656bQ0qUWBw51a8aabXzJSBMRw8c97mOfafSuG2wZ1NkmG/XLKdn9fsNQlkpeZ58cGKgRzNac9b01ty539uYfazvwBQ3v3q0DF5etIAVKLIdyt21TFg4IJh8c4f/zIwpiXuzvWsWHTsDziwPzGAwvK6TVjNIVf/Z9+pVH5ff4B7R9n/d6ZwFXMVe1KaEm9vb7p3ty0RYC9258S0bt3apARq6tSpZGZmNtlCFK5ufjy7wWbeyl0R/fmwy52sGvwyk8LMd9i1xk3BXfitz6OEuvjV6zgJmW3ZtivjVIJIa/dAq2MGREdarb5RiYbQUY2H5X8LtxrLcGuOq7mxHT6bxsTX5/DeXxs4kZRJVkEJxxLTeev3dYz5z498s3wHe+NTzJ/oEuDlZtAJGdKllVkHhygIqFUiM0f3Mnv88K6tCfCyreK84fBZthy5IAp59Fw6P6/ZC5h+T9f8e8XuODYdPsukp25m2mtTkD2d0AV6XFIvTG1EQagTlvFyc+adGaMY2b0tlVodS3Yct/q50Or0rNpT/+pMQfQA1xmUVNRfsE2WZeZuOmQz/KlwDXCd58RcSuz2xFzsgl65ciXvv2+tKZfCtUKRtpwV5w+wPfskVZKeaK8wJoX2oFRXwZ+J29icGWe1ASZcaHEwuFmHRq1lZdohUspy6n3csQLrxoBKEBkWGI2vDdG9gZ1aEhbgxfmcuk/dYDBQaoTGissqWLjtqEX1VpkLpdI1I2qG5haV8eOqPVbX0tQUlFQw45N5gEG7Ja+4zNDnp7rrb5CvB+/MGEXrEPNGpEoU+ePluxjznx+o1FpWM1aJAnM3HWJgdW+e71futrouAfjt3/10aRXC3W/cSquxXXjsh4bn19U3JCnJMv2jwpnQtyM5haX4e7nROyrcqIibW1RGeZX1ykuVSiQpM8/qmBqKSivIyC/GzdmRYF8PBLenaB7UsCThnMJSCssqbLZDUFC4XlGCqTc4JwpTeXzvT5ToKoxf/EcKkvj93BZAqNZdsZ3QKwBlNgwde/gnZXe9b0KA1V5HAgKhLr48236szXnUKpGvn5jMI7P+ITmrAJUoGA0PURB47e7hdGvdHIDDCelU6erfdfVqIK+4DFGAu4fHEuDlRnN/L1SiSEFJOWm5hQT7epo9ztfDhRaBvhb7MYHBI1V7/4HT1hOPZeDQ2TSGPD+bzi2DuXVgTIM+AwCxbZqTVVBiVN+NCPQmu6CE0grLRohKFAj29WBgJ9O8Kq1ej04v4WyrSgrDQ56Lo3VvSkZeMV8s3sba/aeMAoXtwwJ4ZGwfekf9BzenWZRWSMj1rNRzUJJ7r3mu98TeS4ndRoxQ3Tjs4m0K1y4l2gqe2PczpbpKkxvGBc+CbFE47mL0skQL14BGr8nQTLJxCIBGUCMhE+DkwcTQHkwO64mb2r6cg2BfDxa8djdbDiew+chZKnV62oT4M6FvB3xrJbraUsq9GnBx1FBepa2TxGr4HQtsPHSG/tGRfPbPFrS1ynd7tw/jlTuHm21W6exoWXmzhsLSCm5+5QeGdmlNWaX9+lGHzqZx6GwarYJ9LfYOskZKdiEr372PgtIKBMDT1Ylvlu3kp9WW+x3pJZnxfS6I7x04ncrPa/ay40Qismz4PIT4eXI+p9DiefWSjLuzA3PW7sXd2ZHBMa3w8bhQYZWRV8y0D/+koKTc5JpOpmTx+FeLmTm6JyUVht+JvYiCQHRkEK711KlRuAppinCQYsRYR5Zl7rnnHhwdDYlrFRUVPPTQQ7i6msbIFy5c2LQrVLhkrEw7SLG2vEk++2pRxU0hjdcJctc4U2KlqaQ9yBgM7B0j3mrwHBqViqFdWzO0a2uLY6LCmyEKgl3doC83oiAQFd6MY4kZFsdIskxSVgFJ6w/U2bczLplpH/7JvFem4edp+jc+OKYlh86et1ndk55XzO9m5raHcxl5aNQqJK2uPnp2ZOYXk5JdQFjAhYqHO4d2ZdXek2TkFZk1im7p34k2zf0BQ+fvV+esRhQueODSc+t23jXH54u2GXooSTIfzN3IHUO68MTEfqhEkS8Wb6tjwMCF8OLPa/YaE8PtRZJl7rWjh5KCwvWM3Ym906dPJyAgAE9PTzw9PbnrrrsIDg42/lzzUrh22JVjW73WFjXJvv/XYSIeGmeKteUsTN7Nl/Gr+f3cVjLKC+o1383BXWwmENvzrFopaZEa2d7AFv6ebgzr2rpevYQuB6IoMKZXe6YMiGnUPPnF5dz+3u+sP2j6ORnXuwMeLk6XNO9WL8lM6NOxQU+XccmmoS5PVyfmPD+VIZ1bG3tgAXi4qHl0lMQLo/cily8nr7CAN35da1Ho0B4kydDiQS9J/Pbvfsa88iMzP11gtUkngE4v2W2siYIhzPvi1MH0j1baEVwPXO+9ky4ldntifv7550u5DoUrgF7WN9oL08k7nPtaDqanX2v+Tt7F5ydXopV0qAQRSZb5In41t4T15Ol2N6MWbcfuJ4f15J+U3RRpy+vk4qgEkTBXP3r7tmFe8naLoS4BQ9dx0UzDycSSLBan7iOpNBtXlSNDAjsyIKC9XWszx0u3DeFMWg7nMvJMevAIgoCjRkWVVn/ZPTWfPzyefh1bsONEYqPnyi0q4/nvlvPY+L7GUl4PVydmPzmZOz/40y7NmYYgAHHJDat+NCcA5+vhyoczbyansJQzqWfRlH9Oh8AdOKgFqBCQK3Q46dzp1HwoB5Ia1sPFHJkFJWQWlNgcZ49HTwBiWgYT26Y5k/p1ItDH/s7wClc5Vyic9NVXX/Hxxx+TkZFBTEwMX3zxBT162C7Znzt3Lrfffjvjx49n8eLF9T9xE6Ik9t7AdPQKY3fOGatJseYQEenqE8H7ne/A08EQ91+TdpiPTlzoiqyrZYD8nbwLjaDiqfY325zb19Gd73o8wAsH/+BcaRaqare+hEwnrzDe63w7Zboq/kraZmUWgSm12h3U8MOZ9Xx3Zj0qQUQvS4gIrM04Qku3ZnzR/V78bFQuGa9N0rMh8xhLU/eTWVGA71B32me14vDBbHKLSvFxd2Fyv06M6NaG575bbjUJ1hLNvN1o0cyHPfEp9TaCQvw8WX/gNKv3xdf7vJb4csl2MvNLeGJiP1ydHDh9PueSl/aWVWoRRbFeuUcatYrY6sRrc/h6OODT7GXQJcBFvbQdxBJm3bmcO7+9haTcxglw1RdZltGoRINHxtIgwZAzFOzrgb8dpe4KCtaYN28ezzzzDLNnz6Znz558/vnnjBw5kvj4eAICLOc3JiYm8txzz9G/f//LuFrL1Lt3ksL1w/jmsSbudXuRkLiv5RCjASPLMrNPWxY/lIF5yTvJr7L9RAoQ7ubP3H5PMrvH/TzYajiPth3Jb30e49ueD+Dr6E6oqy+PtDGIsV3cB0hEoFN1iXhtVqUd5Lsz6wGMHp6a20ViaTbPHfjNLq9Cma6Sh/Z8z38Oz2Nf7lmSSnM4ePo8K3bHkZpdSHmljvS8Yo4nZSIDc56fyn/uHEanFkFm1Xst8dyUQbQK8atXyEYQoGWwL09+vZjnv19eJwzUWBZsOcyMT+ZRXF7JT6svbXm4DLQI9KnXMYIAk/tF4+FqJYG7cj3oTgN1q8pEUUYt6rmz95G6x10GXpg6GEEQLIYnaz6eK/ecNOruKFwnXAGdmE8//ZSZM2cyY8YMoqKimD17Ni4uLvz0008Wj9Hr9dx55528+eabREZeHaFMxYi5gQlw8uTNTrciIhh1XuBCnktNywCh1nYBgZc7TKCb74UP8OniDM6XW9fI0MsSmzMtK8VejCAIdPWJ5J6Wg5jWYgBtPYJN9k+PHMg7MVOJdLvwxOChceaeloP4ovu9OKouVNDIsszPZzdZXduJwlSOFCTbXNfHccuMmjQSMlKqI7rdHlB4wakpSTIbD53m1nd+Y8rbv/LOH/9y5Fw6bZv70ycqHAcfCbFzEaqROahuykHVqxB1oBZBMHgSXrljKEO7tGZMr6h6JXrKMhSWVNidiNoQEtJz+fTvzXa1H2gMLg5qOrcMtssLU3PTHxTTkqcnD7A6Vq5YjbWvPbVKZkSHpjX+bCEIMK5PByb378RPz02lc8tgm8f8sf4A2mu0vF+hLk2ZE1NUVGTyqqysK31RVVXF/v37GTZsmHGbKIoMGzaMnTt3WlznW2+9RUBAAPfdd1+TvwcNRQkn3eAMD+pEmKsffyVuZ1v2SXSSnijP5kwN70M3n0jWpB9ma1YcVZKO9p7NmRjaneYupl2T7akmEhEobWTV0cWMCIpheGAnciuLqZL0BDh5mM1tSS8vILHUek8klSCyPfskMVYaROZVlrA67ZDRgyPrQDpc08bA9OlZkqG8UktK9oWy3LjkLCRZRhVVjjq8yjiPEKBFblZAL3U7PuhzK+4uBk9Ci0AfnBzUxmaDtpjSvxMLtl5aL4IkyQ1Spq0vZVU6Ppq/CQe1SJXOuiHTpVUID43pTZdWIbZlH6QSLg4jXYyzg/n3WwBcnByoqNLWu/T7YtQq0ZAELMuM692B/7t9KACdIoMY0KklB8+kWQ0jFpZWcCYth/ZhDWuap3CV0YQ5MaGhpv3hXn/9dd544w2TbTk5Oej1+jpNF5s1a8bJk+b/vrdt28aPP/7IoUOHGrnQpkUxYhRo6xHMG52mmN03Oawnk220EGju4mNTnExCJsy1fu0E7EEQBPycLmiZ5FYWo5X0+Dm6oxZVyLLMJ3HLbM8DaCXrT7aHC5JMko3lNEfQCdir7VFzU9KfcIWASgRPw/nk6keovbqTHCw5xwCX9gBsOHjGbgMGYJmVnkhNyeUU+LNlwAjAoTNpNPf3sk+3Sh0JVdsxF06SZUjM9eJctqHcukboUK7+9xt3j6Bzy2C+X7GbFbtPoJMMsnSqaoPEntwlUTAYXV1aNcfdxZGhXVrVERbUS5JdssONNaQUrk9SUlLw8LjwnVgji9IYiouLmTZtGt9//z1+fk3/Pd4YFCNGodEEOHnS268Nu3PPmFX3FQBvBzd6+7W5ZGtYn3GUn85u4nRxOmAILd0S1osYr3C7+irpZIn2niHWB110k5JLVfXXuAcQZKREZ1QxpjlCIgJzE7czIKA9ekli4faj9Zq+woY0viX8PV0pKa+kvB4Gky0cdVVUqu0XYavv+BpkDMbhwm1HeWhM3WTuixFcbkUum1Nn++b4CL5a34OEbIOXUQD8PF1p2zyAzi2DGde7Az4eLqTlFoEAgiiCpEcUBXq3D6ekopJDZ9Js/q4k2SDIJ8swoW9H/GqJJ1ZU6dh/OtVQ0WbDQHF20BBZz5whhauYJvTEeHh4mBgx5vDz80OlUtXpf5iZmUlgYN3+cmfPniUxMZGxYy+onkvVoV61Wk18fDwtW7asc9zlQDFiFJqEB1sN51B+EhX6KpNqJxGD0vNr0ZMbXMZsi9/PbWVW/CqTJN8ibTlzzm7CQ+NsrEayhpfGhUE2+j519ApDRLgQBnKQ6yXEZkQWkAvr/ulJyBwtSOZUajZPfbOEjLziBkxePxzVKqLCm6HXS2w/ntgkwocTT+xk+qGNPDjuYTLdbFf5NCvJ59ul3/BL58EsirJtiFyMJMscPZdu11hB3QrcHkcu+YIaC3Tlkda8tmgYQq0wk4yhL1FZ5XmeuWUgPh4uJGflc89HcykurzR6QfSSzI4TiTio1cx6bCKuThrW7T/FXxsPmRWvE4CsghJyCks5cOY8czcd4usnJrFw21F+WrXH2GvLGqIgMLFfR1wUpd7rhsvddsDBwYFu3bqxfv16JkyYABiMkvXr1/PYY4/VGd+uXTuOHj1qsu0///kPxcXF/O9//6sTwrqcKEaMQqMoqCrlfydXsib9sElZdQ1dfCJ4sPVwOntHXJLzny/L44v4VQDIF92CJWQKtGV2zfNBlzuNicyW8HfyYEhgRzZkHjM0TgyuhGMNKXWVQWVJ40Zg5mcLKLXjZtYUVOr0bK7VcbqxOOqqmH5oI2FFOXy39GseGPeIVUOmWUk+3y39mtCiXKYf2sjKNt3q7ZERMDSntHu82+OgCkMumU1FRRIfrBiA4dNjOodekimrqOLzhVv49KFxvPvnehMDpva4Sq2Oz//ZwvxXp9G5ZQgDoiP5Y8MB9p8+T5VWZzym5sia0NPJ5Czu+2Q+Z9Nzba+7ulFndGQQj47va/f1KiiY45lnnmH69OnExsbSo0cPPv/8c0pLS5kxYwYAd999NyEhIbz//vs4OTnRsWNHk+O9vLwA6my/3ChGjEKDKdaWc/+ubzlfnmfW0zElrDfPR9luutgYlqTuQ0CoY8DUBz9Hd7r6tKBK0rEkZS9/J+8mtSwXZ7UDI4NiuD2irzGZ+aUOE0gqzeF0cTqis4QUUYGc6ER9et4AiEF1KwZUgoi/1oek8qor0MpAxp5rmDasK7/9a7mVQKXagQfHPWw0TKwZMrUNmBQPXx4c93CDQkoAgd7uPPHVYuKSM3FQqxjSpTVTB8bQ3N/L7PhzBf2Yv9md9QfjKauynHCul2S2HEngSEI6e+Mtd0qXZJmz6bkcPZdBp8ggerYPp2f7cLIKSrjp5e9tHmcJAXB0UKNWiYT4eXJL/06M7RWFg0b56r6uuAJid1OnTiU7O5vXXnuNjIwMOnfuzOrVq43JvsnJyYj1eDi4Uih/CQoN5o/EbaSW5VoUy1uQvJNbw3sTfgkSemtIKs2ut1hfbUQEJjTvToVeyxP7fuZw/oWQilZbzsKUPSw/f4Avu99LtFcYHhpnfuz1EKvSDrIkZR+ZsYVoHVTknzYYcaJwQZzNbD6LIIODjBBa14jRyxJFx1RIcsNyWxqMpxYkAYpVWDJkBAw5HCO6tWXxjuMUl1nuWJ7p5s0D4x6xashcbMDY8thYRYC/tx4xCd/M3XiQv7ccZtajE+ne1tTVvXz3Cd74ZS2CYF9yrCTLHE5Is2spCem5dIq8oPi7Lz6lUfcmGUMi9doPHsDNufEJmgpXJ1eqi/Vjjz1mNnwEsGnTJqvHzpkzp/4nvARc/WaWwlWJLMssStlj1YBQCSJLU/dd0nU4qxxMNG4sYa4fk0oQ8XZw5ZawXvx0diNH8pPqXI1elqjUa3nx4B/oqquXnFQaJob2YE6fR1g19GX+ffpJVr57P8/dMoiZo3vyzoxRzHpkAs5ODtWhjgsCZt7uzrgNKEPUmK6jRn+nKsu+P0mVeLHMX8MR25UhtCi3OkYGdp5IZNqHf1k1YGqoMWRSPHyNhkyzEoO2TJMaMFzIt65tkOglmSqdnqdnL6G4/MJ6z5zP4Y1f1iLJcr2qe3zcne0a5+xg2uF7zf7GqyZLkkxBifXfj4LCjYriiVFoEFWSjvyqUqtjJFkmzYYIXmMZEtiRlWkHLe5XCSKDm0VRqdexNftk9Y3fEH5q7R7Ie51vx13jxD/JuywaZBIyOZXFbM2KY3Cg+fhvM293bh9i2sV7zfszWbk7jiPn0lGJIr3ahzG0S2tKpQqWnd/PtqyTaCU90V5hTArrSbirH4ubJdiVpNpk5bUaCaFZFYIM+gwHyKoJ59SYSNVhJp+qWonG5s0njVpEW6sk2pxH5tUhd/D2hj+bzICxhixDeYWWFbvjuG1QZwDmbz5sUEG28+0TBGgXGsDwrm34eP4miqwYcBq1it5RF3SGyiu1VkNQ9iII4OVmnxGlcI1yBcJJ1wuKEaPQIBxENQ6imirJclmuKAh4aFwu6Tr6+rellXsg50qy6uTlCAiICMxoOZjW7kEkl+awp7oMPNorjChPQ4+dpNIcim0I8akFkROF5y0aMeZwdXJgysAYpgyMMdnuhSvTWgxgWosL6rJ6SeL0+Rz6dAi3u9LmrqFd+X39AbNVMHajkQ03dQFUPYuQzzojJThDRXUlmZcOsVUZcp4GOc96vopWJ+HsoKayVtPLiw2ZOYu/ALjkBkwNgiBw+Gya0YjZfTKp3u/VY+P74aBRc++oHny+cKv58wC3D+ps0vIgIT23Xjo/lvD3dMPFUalEuq5RjJgGoxgxCg1CEARGBcWwIu2gxfJlvSwxMijG7L6mQiWIfBE7g+cO/MbxwtTq0IxB98VV7cj7nW+ntbshRyHM1c+s4J5GsF36LQPqRiS5lZRXsnLPSeJTstCoVQzoFEmvduEIgsE78POavWRVdzu25SgQgLBm3jw9eQB9OkTw18aDHD2XQVllFVXaegrRVQnIEghi9at1OUKrcqgSQARBYygjl87a5wkor9IhCoKxkgYMhsyrQ+4wGjAArw65w6YB0yjjrBa1+4PVJ1/a3cWRV+8cZvSuTBvWjcLSCuas2YsgCoiCgFwdlprQtyOPTehncrxd4ntA6xA/Tp/Psbg/q6CEF35YTvc2oQzv2gYfj0v7YKCgcC2hGDFXEcXViaTLUveTV1VCgJMH45t3Z3zzWFzUV19S37TIgazLOEqlXlsnFCMiEOvbki6XqLS6Nr6O7vzU62EOFySxNeskWklHG49ghgVG46TS2Dw+yNmL5s4+pFoJfellid5+bRu0vs2Hz/LyTyuprNIZS4Hnbz5MqxA/urYKYf7mwybjbd1nZeDOoV0RBIFe7cPp1T6cWYu2MmdtA/KPdCLyeUcIqaQmtUgQAMdaq5CBQtvvYw39OrbgeFIGuUVlCEBAST5vb/jTZMzbG/606okJ9fOkSqcns8C+pqGWkGTZJLG3R7sw0nccs2gcCQJ0jAjizqFdGNSppUkVkCAIPD6hH5P7d2LF7hNk5pfg4+7C6B7tiDAjPNcq2BcPF0erISiAIB93zqTlWDWwNhw8w8ZDZ/hkwSamj+jOI2P7IFpoFKlw7WG/7rf1OW5EFCPmKiG7ooiZu78lvbzAWC5cWlLB5ydXsiR1L7N7PICXw9X1BBbu6sc3Pe7n5UN/kl5eUP1kalDcGNSsA69FT7b7abSxCIJAZ++IBunRCILA9JaDePfYQrP7VYJIe48Qor3qL+h0IimT575bZuiTA+hqNTRMSMvhjJUncMPaLngPRFFAkmTG9GrPpL7RxjHbjyc2zICpRj7pasiLcTDV2pFlw/mlk66G6iU72XI0gRduHUSInyd71u3g1jffNebA1M6JsVR+LQCdIoNZsadxbRREQcDDxZGR3Q3GZ1puES0Cfayq4QoIvD5tOJFBvhbHBPt6MHN0L5NtWr2exIx8JEkiItAHR40aB42a2wd34bsVu8wapipRoFOLILILS+3yEMky6GWZn1bvQaMSedAOhWKFawQlnNRgFCPmKuHNowvIrCg0vYlU/zepNIePTizhvc63X6nlWSTKszmLBjzHntyznCpKx1FU0zegbZ0mkVc740K6kVKaw6/nthgVfmvUecNc/fio610NMsh+WWcwLsx9v9gTKXFy0ODh4kSlVkfrED9uHRjDkM6tTNYyd+PBBode2jT3Q0DAIzmcHN8skj3PGz0yVAroT7oiJ9U/qfTjBZtZ8/BI+n75OuJFSbzWyq9VooC/lxtn0nIQBaFeejm13wNBMOQkffn4JErKKnnp+xVsO3bO5PdQ20Cs6ZP05vSRJgaMVq+nvEKLq7ODWUE9vSTx67p9/LH+AHnFhgoit+pcqAdu7sV9N/UkIT2PdQdOGddXc95Qfy8+nDmG//tpZb27V8xZu5c7hnbFXSm7vi64UiXW1wOKEXMVkFSSzZ7csxb362WJDRnHyK4owt/Jek+MK4EoiPTya00vv9ZXeikNRhAEHms7ipHBMSxO2UtiaTZuaieGB3ViUEBUg1omyLLMxkNnGpXXUV6pZcunjyDLhhLn1OxCVuyJY2B0pLHb9dFz6Q0+x6nUak9QquF/osofdYCeygo9FGhAbpgnLaA4D2nQIMS8LEqCm/PAkBlGQ+ViQ+b7pV/z0PhHSXP1olWIHx/NvJnxr82x6zwatYp7R3anX8cW/L31CCeSMnHSqBncuRXj+3ZEFATufP8PMvOL6xgJsgxODmrC/VXc3qeA3u288PPJQJZbk5hZxI+r9rB2fzw6vYSLo4bxfTpy76ju+Fb3O5JlmTd+XcuKixpvllRUMWftPo4nZfLFYxP44P7RjDvRgUXbj5KcWYCXmzOje7ZjVGw7nBzUDO/ahn2nUuv1/lZq9Ww7eo6berSr13EKCtcbihFzFXCs0HYZpoRMXNH5q9KIuZ5o7R7E81HjmmQuvSSj01vv2WQLZwcNO44n8tbv6ww5JtXJpA5qFfeM7M4Do3vVS3LfFpIeKtNVCK71S4KtTY0OTEBRLuXNw3DbsY1Hzxfzv0VbyS0ytIHIdPPmzXtf5rN5n9M8PZU/1/1I6vxFtO8XiyxjlxcmMtCHoV1bczYtl7jkTHzcXXn+1kF0a93c6Kn6bsUuMvKKzc4lIHN3nx3cP+AIoqAFROQCCZ3sw1cL+rI5PtRoHJZVapm/+RDrD57mlxduo5m3O3viU+oYMDXIssyek8ms2RvPmF5R9O0QQd8OEWbHju7Znp9W7yGnqNRuY1QAE/0bhWscJZzUYBQj5irAHrG2+oxTuDooqazAxUlDWUXDFHhVokD3tqE8/c1SY5hRrr4ZV+n0fLdiF+dzChnQKZLlu040nXYM1R26Q8rhfE3JsH0eGUddFd8u/YbQolxSPXz57sFXeSs0lDGhMKp7Ow6cTqWgtJwgHw86RgQiPHMrDBqER0ICUffeAUeOIDg7071tKHtPpVjNX0nIyCNh5W7jz6IAi3cco2e7MMb2jiKnsJQ/NhywaAw9PGQ3M/od5EJkrlp1mTzev2U5j/w2lv2JFzqb6yWZ3KJSPpq/kf8+OI6F245aDeOJgsCCLUcY0yvK6nvm6uTAt09P4fEvF5GSXWBXaFAGmvt7Wh2jcI1xgxohjUUxYq4Cuvq0MOmObA6NqCLaK+wyrkqhoRRWlfFt/Hrm/3kSqcKylL81REHAUaMmLa8QsOwVWbE7jmZebpem19L5+veEqlQ78EvnwcYu1pWFBp0UrU6PKAr0aGf4DMuyTEp2AUV6DSHLVuI9fgy88AI4G/Jv7h4ey+6TyfU6d819f/fJZHafTDbJebkYb5cy7u5zCHNpTqJgSKB9dMgu7v1pssk+vSSz+XAC2YUlJGXmWzU2pOprtIewAC8WvjGd7ccT2R2XzLoDp8gtMp/wKwgG7Zie7ZTvAwUFxYi5Cghw8mRYUDT/ph81a8gI1f19PDSKaufVTkFVGfft+obk42VIBa40tPCxmbcbz00ZxLPfLrM5tnYZ8sUJojVhmZqne1GwL6H4wmz1Z1FUb2M3alednlve+oWE9DwEAXq1D6drqxDWHTjNqdRs4zEuQ2YSlu3C9L3xDO/Wht5R4Tx7y0A+/XszYgOTlq3ZdUOjEhBFKy0zROgUmkWQZxHphaYhXEmWOZeeh6erk1VDCahX4q1KFBkQHcmA6Egm9O3IjI/nUlmrAzZg1OB5bdrwJg0jKlxZlMTehqMYMVcJL3eYSGZ5IYcLkoxemZr/9/RrxRNtb7rSS1Swg29PryOtPB/9uYa5+kVBYGiXVrx/383EJWfW71hRINjXg2BfD9SiSK+oCMb2bE9iVj7Ld50gt6gMf09Xdp5IIjWnsEHrs5eabtSlFVUkpBv0d2QZdsUlsfNEUp3xZaKGkylZvPzTSjYcPsN7997EnUO70jsqnL+3HOF4UgaFpRUkZxU0yfq8XCrQSyKiynrOkpdLRR0jBgxVYzd1b2e1rYAoCNzcs32D1tc6xI9fX7yd/y3aaqiqqr5BdYoM4rHxfenaunmD5lW4SlFyYhqMYsRcJbiqHfmmx/1szT7J8tT95FQWE+jsxbjmsfT2a42o5MNc9ZTrqlh+/oBBwbhMpCGeDEEQaO7vhSgKBHi71av0VpJkUrML+fzh8SZlwjFuzsREBht//mfrEd79c32912YOjUpEW4/kZXuiXuv2n6JLy2BuG9yFyCBfXpg6GIDZy3fy0+o9jU6WBkgvdEMtWp9HkiGryK3Odh93F6LCm9G6uR9z1u7lfE5hHU+RShTwdHXilv6dGrzGyCBf/vfIBHKLSskqKMHbzYVAH/cGz6egcD2i3BmvItSiisHNOvDfbnfzS59H+bDLnfT1b6sYMNcIWRWFVErVSbwODXss0ksSPaoVZv093ejTIaJeppAgCGY9HbUZ36ejxUqZ+nDn0K68effIRs9jjt/XHzAmMdfg7+mKvgkMGIANJ1pSrlVbNKp0ksD202HkltYVmLxvVA/UKhFnBw3fPz2FqLBmgGm38lB/L75/5tYmaRHg6+FK+7BmigFzHVMTTmrs60ZE8cQoKDQRTuoLTfqE0Ark0y710llRiQJhAd7G5FeApyb1Z098skl3aGvIssxfGw+SkJHL5H7RRIUH1hmjVol89vB4Zi/fwU+r99q9vto4qFXMvKmnTYOpoaTlFlFYWmHSvXl41zZ8NH8TWl09+0OZoUKr4b+r+/LquM1IsiGZtwadJFCpVTNrXe/qvksygmBQS75nZHduG9zZONbfy41fXrydY4kZ7DmZjCTJxLQMJrbNhTJvWTaU2qtVYoMEE3OLStl9MpkqnZ52oQG0Cw1o5NUrXHUo4aQGoxgxCgpNRDMnT9p5BBNflI7Yohx9ojNosWjI1CTZ1uz19XDl80fGm9zoWgb78f3Tt3Lff+fZndyallvE0h3HWbTtGHcM6cKztwysc/NUq0QeG9+PZl5uvD93o93XWJMo/NJtQ/BwdeJStu9RqS54ILMLS9h1IokB0S1Yf/BM4+YVBQQElhyMoqzKgceG7iLEu9i4/2BSEJ+s6se5HB/AkBTdpVUIr945jNAA872eOkYE0jHC1GDMLizh13X7WbL9GCUVVbg5OTC+b0fuHt4Nf8+6YaqLqdTq+GjeRpbuPG7yu48Kb8a7M24ivNml7QCuoHAtoBgxCgpNyMxWQ3n2wG8ITqDqX4B+rwcUqbnwmCTQJyqcSf2iWbU3nnPpubi5ODIqti1jekXhZqaapVNkEN8/M4VH/reQCq3OrnXU3PT+3HCQ8ABvpgw03038lgExpOUV88vafbUqmAyGSotAHzpEBLJufzyV1d2x24b68+CY3gyIjgRg7YFT9XuD7EAQoE2IP+7OjlRU6fhw3gaW7TxhUkZe00eqNo4aFVqdZLPcXC/JREc040RyJuuOt+Lf4y1pG5SDu1MlafkenC/wqDN+36lUluw8wWPj+9p1DanZBcz4ZB4FJeXG30VJRRVzNx5k9d6TzHn+NkL8LCd/y7LMSz+sYOvRc3WuJz4lixmfzGPuK3cR4GXbGFK4+lGqkxqOIF8ceL6OKSoqwtPTk8LCQjw8FOVbhUvD4pS9fHxiKbqa/kt5avT5KiLdAnhz6HjaN68b4rGHzPxi/tp4kN83HEAy5obYdoUE+Xqw7K17OZmSyYZDZymv1NIy2JeRsW1xdTKEwE6lZrNo+zGSMvNwd3ZkWNc2ODqo2H/qPFqdjtAAbwZER5rceFOyCxj/2s8NuhZbvH/faEZ0a8OTXy9hx/HEOjdyUQBnRw1PThxAxxaBtA72Q6USmfnpAvafti7hPzimJbcP7sIDn/9drzWJosCq9+63y4sy89P5HDqbZtZ7VuPZ+e7pKRaPP3jmPPf9d77F/SpR4LbBBi+bwqXjUt8zaubvNOM9VA5Otg+wgr6qgiM//98Nd39TPDEKCk3MhNDuDGrWgVVpB0kuzcG1hSPDAqNp5xli+2ArNPN256lJA9jvd4S4o/lIZ52g3LaYXnpuEfd/Oo9DZ9MNoRRBQKeX+GTBJt6aPpJhXdvQprk/L1ZXASVnFfDEV4tIzipALYoggE4v8cvafXz+yHhjTsbmIwnGNghNyczRPRkZ25YDp1PZduyc2TGSDBVVOs5l5HLLAEMFUEl5pU0DBqBViB+xbUN5YmI/Zi3aZn/zTBnW7I3nrmHdrA47l5HH/tPnLe6v8ewkZuQREehjdsyK3XFW16WXZJbsOK4YMdcLSk5Mg1GMGAWFS4CXgwu3R9QNPaTlFHEo4TyCIBAe4E37sIB6J3s6aFQI3lqocLX7mMMJ6UBNmMnwbVdZpeOlH1by7dMudKvWHSkpr+SBzxaQW1QKgE66kFCcU1jKPR/NxcvNibzichzUKpr6m7NHu1DuGdEdgBV7bN/Il+08wfO3GoyvojL7egnVjLtnRHe6tAxh7qaD7IlPIb+6C7UlRFEgp/p9sUZtET9rxKdmWzRi8orLbBpWJeWV6CVJEb1TuKFRjBgFhcvA1qMJfDR/I+dziky2hwd48393DKV7dVm1PfTxb8uhZcXV9oN9BpA5Z4mMIf/kx1W7jUbM8t1xZBeUmDVNJFmmSqcnq6DawGmicufa7ItP5fnvlvHFYxMpKC63fSOvqDLeyJ0dNHadQ1UrGzmmZTAxLYMpLK1g6POzrebT6CXZrhwUg3FnG0eN6devVqdHJ0k4adQ083a36SHydnNWDJjrBCUnpuEoRoyCwiVm2c7jvP7rWrP7krLyeWTWP3zz5GRi24QiyzJHEtJZsuM4abmF+Li7MLpne/pERSBW33xjndog5x+2+/zWBPMkSWZXXDKlFVW4OjmwZu/JK+qVlmSZHSeSOJKQbpfBIApCdRk0JGfn23UOT9e6uQeerk4M6BTJ1qMJVhs6joptV2d7clYBS3ceJz23yDiPLRFAR42Kbm0MhuOOE4nMWbOXfacMobCwAC+Gdm5t1YARRYFJ/aKtXqfCNYQSTmowihGjoHAJKS6vtKmOq5dkPp6/iT9evoM3f1tnkg8higKr98XTrXVzPn9kPK5ODujK7D+/n6creUVlNvNWHvzsb3zcnTmZkmX/5JcIVfU1x7QMZt5m68aaJMscOZduokhsDQGMycwX89j4vuw5mUyFVme2e/aDN/cyEa+TZZkvl2zn5zV7UYkCsmwQG5y76RDN/TwttnYQgKmDOuPu7MjcjQf5aP4moyEGkJJVwM9r9xLk405GXnGde5NKFPD3cuOOoV3tumYFhesZxRepoHCJiE/J4smvFlNlhzjb6fM5fDB3Iyt3xwEXSqRrbqYHz5zn7d/XAditAjuudxR3De2KbMcj2onkTLYfTzSWUl9JZBmKSivsDpWkZhuMhTYh/jg7Wg8pyWCx71BkkC8/Pnsr7S8Sk/N0deL5KYO476YeJtvnbT7Mz2sMYoF6SUaSZfTVOURpuUUE+xoqRAzJ1BfCWKO6t+Ox8f1Izirg4/mbAEzCWDX/Ss8rZkCnSFxqXZMgQL+OLfjl+dvwdlMawl4vCLLcJK8bEcUTo6BQDyqqdKzdH8/244nodHrahzdjQp+O+HmaJtl+v3IX3yzbWa+5V+yOsxz2kWXWHTjFE7n9aRHoQ7vQAOJTs6z2IvJwcWJU97Z8tWS7Xf2NrpavQFmWCfHzxMPFvg7Q7tXjnB013NK/E3+sP2A2t0UlCnSMCLKqeNs2NIDfXrqDM+dzSMrKx83Jga6tm6O5KM9Fp5f4cdVui/NIskx6XhH/e2QcO04kk1NYgp+nG2N7tTeqKC/cdsRqh25REMgqKGHdRw9y+GwaVTo9bUL8lfYD1yNKOKnBKEaMgoKdJKTn8tD//iGnsBSxurR485EEvluxi7fvGcXI2LYArD94ut4GDBgUWq0hy7A7LomJ/aJ5evIAHp71DyBbNGT+3HCQuOQsXrxtCO/88a9RxO5qRwZGdGtDeDNvPF2dKCytsDjWzdmRnrXaNDw6rg+nz2ezKy7ZeL01gZpAHw8+nHmzXWtoFeJHqxA/i/vjU7LILbIR15MhM7/EWLped45sq3kvkixzJi0XZwcNvdqH27VuBYUbDSWcpKBgB+WVWh78/G/yiw03Lkk2BGmk6r44r/y0iuOJGQD8uGpPvedvZkcSqwBGj0r3tqHMenQCvu6Wy6wlWWb/6VQkSeKLxyYSFd6s3uu6UqhVIhq1iofH9rE6buboniSk57I7LonkrAIcNGpmPTqRD+4fTWzb5gT5eNA+vBnPTx3cJAq3VVod5ZVayqu0NscKgkCFlfCck4MaW9X19lY6KVzbKA0gG47iiVFQsIPVe09affIWBPh9/X5emzai3smxAvD4hH68/usaq0/mMtChliHSJyqCAZ1asHj7MSwdJggwf/MR5r86jfLKKl74fkW91nalqEm+nTKgExVVWr5augOdTo9KJaKvbqY4tEsb/t5ymM/+2WI8rnPLYF6YOpgR3doyolvbJlvPrrgkfl6zl73xKQAE+bgjCOZL12uQZJnWVrw5A2NasvlIgsX9KlFgSOdWDV6zwjWEEk5qMIoRo6BgB1uPnbN609JLhtCSuaoWa3i4OPL6tBEM7tyK7ccTWbs/3qJUfesQfzpc1GQwMTPfogEDhvWmZBcAhuRhtUq8JPouTUnHiED8qz0mgiBw9/BYJvbtyLoDp8kqKDHkH8nw3l/r66jkHDmXzr0fz+Pn56fS1kLuiyzLHE5IY+mOE6TnFeHr4cLNPaPo2S7MWMZem3+2HuHdP9eb7DNXNVQbURAI8vWgexvL+j8jY9vy7bKd5BSV1vmdC9X/vVOpQFJQsIpixCgo2IFWp7f61A2g00k4O6hxddRQWmk73PD29JGM6tHOWIXz/K2DOJmSRWJmvklJtCgKeLo68cH9o+vM4eHiZDPXxdnR8Gfu7KC5JnJiHhzTu842dxcnoy5KRZWOES99C9R9+JQkmSpZz3//3my2N5FOL/HqnNWs2RdvLGNXiQIr95ykR7swPntonEmFU3peEe//tcE4dw21z3uxcSsKAghQVlHFxDfmMKRLK6YMiDFWK9Xg7KDhi8cm8vCsf8gtKqvuCG7ItXLUqPlg5s20ae5v+w1TuOZRxO4ajpITo6BgB+3DmploeVyMKAi0CfVn1uJtdhkwPdqGcnOvKJMyYi83Z3554TYeH9+XYF8P1CoRXw8Xpg+P5a9X7iIswLvOPNERgTYNkyqtntTsAgZ0iqy3p+hyIgCv3z2cvh0irI7bfOQsJeVVFvdLsqE3UVpuUZ19s5fvZO2+eOBCGXvN//fFp/DeX6aaPou3H7O5Zg8XJ2P3caNBKcvkl5STkl3Ab//u55a3fjHp6yRJMj+v2cPMzxYYw5QyEOTrziPj+7Dmg5nGTuEKNwByE71uQBQjRkHBDib262hV4V+SZfpERfDruv0251KJIo9P6Gd2n5uzI/eM7M7yd+5jz5dPsu7DB3l8Qj+znZMTM/L4cbXtJOIKrY6nvllCi0AfBsW0tGqMXSncXRz54+U7GN+7o82xGXnFJq0DLI8zNWLKK7X8teGg1TL2VXtOkl1QYtx2+nyOVSNRxqBps+Gjh3jtruHGsbVtRUmSqdLqeerrJZRWGIyv9/9azxeLt5tUXsmyQRtm1Z6TiEo7gRsKJbG34Sh/KQoKdhDk48Grdw5HwLT3Ts0/b+7ZnuTMfLuqTWY/NblObktDmL18p82ybDDcRBPS89hzMpm37xlFj3aGPI2rxZZ5ZvIA1rz/AO3C7Kue8nJztqvrtNdFYnBHzqXbrCqSZJldcUnGnx01aptGn1ololIJLNh82OJYSZYprahi1Z44TiRl8s+2o+bHSTKJGfnM23TI6jkVFBQMKEaMgoKdjO/Tge+fmUL/6EjUKhEBaNM8gDfvHsHLtw1ly9EEm3kzHi6OxmaLjaGsoor1B0/bdTMHg/dnT3wKrk4OfPX4JH5+fioT+nTE2VFj1ZhRiYIx5KVRidjhAKk34/t0wMnB/vS8wTEt64jP1UYQoHWIHy0u6hCts0M5GTARBhzUqaVVT4xKFBjcuRU6vURcSpbVsaIgcPBMGkt2HLPqSZJkmX+2HrFrrQrXCUo4qcEoib0KCvWga+vmdG3dHFk2iMzVVKzMWrSVCju8Ii4W+vbUl4LSCrsNGAC9JDF30yFOJGVyy4BODI5pRUxkMI+O68vXy3awfNcJY3uEDhHNmD4sltziMk6mZOGgVtE/OpLIIB+e/GoJZ9NzUYmiQSunEYnCAob3093FidKKKpbvOsGaffGUlFcRGeTD5P6diG3THOEiK8vD1YmZN/Xk62U7zM4ry3Dn0K51jmsT6m+X4F/HWl6yIV1aEeLnSUZekYUKIrh7eDd7m4mDAOdzCm3+7jJrhbQUbgxu1HBQY1GMGAWFBiAIgtGDUanV8fcW20/OggDDurZpkvN7uToZq2vspbxSy95TKew+mcyIbm14996b8PFw4T93DuOZyQPILCjB1cnBqiDcvP9MY2dcEluPJpCQnmvsvNwgBLh/dE/O5xRy/6cLyMq/ULZ8LiOXtftPMbFvR165Y1id0uf7buqBIAh8u2JnnZJxQRB4549/8XRxYmBMS+N2f083hnRpxcZDZ8y+b4IA7cMCTCqCNGoVs5+czCOzFpKSXWBo9IihTFujUvHuvTcZ2wh0ahHEscQMi0aSJMt0bxPKvlMpNn937s72tVxQULjRUcJJCgqNJDW7gJIKy9UyNahEkSkDOhl/lmWZnSeSePqbJYx86TvGvvoTn/2zhbRc892Pa+Pi5MDQLq3tSnCtTU110tr9p/hi0TaT+VoE+thUtBVFgb4dInjptiH879EJJs0J64wVBItNCjUqkbemjyK2dXPu/WQemfmmuis1N/hF24+ZzQ8RBIE+HcKNDRdrI8syer3EC98vJ/2i5N6XbxtCiJ+nWceJLMOZ87n8vcW0c3aInyf/vD6dTx4cy9heHRgV25anJw9kzQcPMLRLa+O4u0fEWjRgREHAy9WJkd3bclOP9lYNGJUoMKZXlMX9Ctch1RVtjX7dgCieGAWFRmJvJcm43lE08zY075Nlmf/+vZk/Nxw0eSr/c8MB5m8+zKxHJ9C9bShFpRWUVFTh6+GCo8b0z/WhMb3ZduwcFVpdg0qnf/13P5VaHS9MHVwn9GKOtNwi8ovL8PdyI8DLDZ1ews3JkTILJeVqlci3T92Ch6sTGw6e5nBCOq5OGlqF+HNzj3a4OTvyzOylZBeW2ljnPqYO6lzHGzN34yFEQUBv5stbxmAI/b3liEklmLe7C7+9eDtPfr2EQ2fT6hxXpdPz3l8bcHN2ZFT3dibXMqRzK6sKukM6t2Lm6J58v3K3ye9UFARcnAyaMM4OGnq3D6dLqxAOn02rY/SoRAFXJwfuHKKI3N1IKDoxDUcxYhQUGklYgBcBXm5k2chjGN/nQvnw2n2n+HPDQQCTp3K9JCPJOp74ejHREYHGcI2jRs243lE8eHNvfDxcAIgI9OGn56by5q9riavV6kAUBERBQGfGS3Ex8zYfJsjXg7uHx1occ+jsef63aBuHa930e7QLw9fDhZxi6wZIkK8Hrk4O3Da4C7cN7kJJeSVllVqcHDRsO3bOqux+DZn5JaTnFRHi52myfceJRJsNFHeeSKpTzq6TZI6ey7B6zq+W7mBkbFu7jLvaPDy2D/06tmDB5sOcSM7EUaNmSOfWTOzb0fh7E0WBWY9O4I1f1rDh0BnA4FmSZJnwZt58eP/NSqdqBQU7uWaMmHfffZcVK1Zw6NAhHBwcKCgouNJLalKKteUsO7+ftelHKNFWEOkWwMTQHvTya13vL1KFy4tKFJk+PJaPF2wyu18UoENEkEnC6G/r91tMMpVlqKzSmYijVWp1LNx2lK3HzvHrC7cbpPeBNs39+eP/7iQ+JYtzGXm4ODnQvW0os5ft5K+NB+1qMTBn7T5uH9zFbMXPnpPJPPrFojoJvPviU2wmyGp1elbtPcnNPdqzbn88i3Yc58jZNGTA2VGDl6uTzbXVYO5c5ZW2E6mLyyvrbNt8+KzZMFRtzucUcio122LrAmtEtwgiukWQ1TGuTg58/OBYUrML2HEiCa1OT1R4Mzq3DK7z934iKYPf/j3A1qMJ6PQSUeHNuH1wF4Z1Vb4brhuaorpI8cRc3VRVVTFlyhR69+7Njz/+eKWX06SklOby0J7vyKm8kBdwvjyPTVknuDm4C69GT0YUlPSlq5nbBncmKSuf+ZsP19knyeDm7ECFVoezgwadXuJEUqbNOS++b+slmeyCEr5cso037h5psq9taIDJDbdTZBC//WtbeA+goKScuOQsOkWa3nglSeat39chyVKdtdjTvkAUBf7ecpiP529EqzM1GsortZTboWwMBi9UkI+HmT12rMHMPb6kvNKuKiVrqsBNRXN/L24d6GVx/+q9J/nPz6sRhAseuyMJ6Rw6m8akftG8csdQxZC5DhAkw6uxc9yIXDN3xjfffJOnn36a6OjoK72UJkWSJZ458At5VaWmiY2y4RO5Iu0g85J2XpnFKdiNIAj0aBdmcf+uE0k89+2y6rH2V+RejF4yqMqa8zDUIMuyTbn8i6nS1fVq7IxLJC23qMH5gnpJ5lRqTh0Dpr6E+nuiVtX9qpLtMGJq5xHlF5eRml1AMx93mwaMAHXCVzVIUuNKy+0lq6CE1+asQZJlk7BZzdoXbjvK2v2nLvk6FBSuZq4ZT0xDqKyspLLywpd9UVHdXipXmj25Z0kqzbE65o9zW7k1vDcqxRtz1SLLMrOX77TY6VoGdp5I4vuVu5g5uhcxLYM5XB1aqS9avUR6bhHuFpoDHj2XwfbjiXbPpxIFIgN9TbaVVlTxzu//NmB1TU+v9uFmtwd6e3AuI8/icYIAof5ebDp8hjlr93EkIR0wGDaOGrVFtWOVKNCzXZhJXkqlVseCLUeYv+kQqTmFOKhVDO/WhruHx9I6xK8RV2eZRduO2hTP+2vDQUbGtr0k51e4jCjhpAZzXd8V33//fTw9PY2v0NDQK72kOhzIS7BpnGRVFpFRXnB5FqTQINLzijhzPsem1+KbZTs5dPY8I2PbNuo7x9lKabOhQ7N9f9oqUWBY1zbGpFMwhFvufO+Pq0ZwbUJf8/2UJvbtaFVtWJYNyb/PzF5mNGDAYJBoq4X9Lj5cJQq4ODnw/K2DjdsqqnQ8Mmshn/2zmfM5hvL3Kp2e1XtPctf7f5i0KWhKjidZ1pwBg0fmRLLtsOTVRGp2Ab+u28fs5TtZszeeKjsEIm8ElN5JDeeKGjEvvfRStWiY5dfJkycbPP/LL79MYWGh8ZWSktKEq28aZNm+0II9rnOFK4e9+R0C8Pv6A+yKS2pQ7yJBgFbBfjS3EOoAKCqrwJ7HMgFD9dDzUwYZt+n0Eo98sZDk7AK71uPm7IBGJSII1T2EqpNQmipL4/bBXYgM8jW7b2K/aMKbeVvVyqnUmm81IMkyoiDg6+Fq3CaKAoNiWvHbi3cQ3uxCx/A5a/cavGYydbRsdJLEi9+voKKq6W/GKlG0+Rmx11i90lRqdbzy8yrGv/YzsxZt46dVe3j5p5WMeOk7thy1XaF23aPoxDSYKxpOevbZZ7nnnnusjomMbHg7ekdHRxwdr27ly84+EfxybrPVMX6O7gQ6eV2eBSk0iEAfDxzUKqN0vyVkYOfxRCq0ugZ958gyPDiml9VkzmBfD5smjADcO6oHdw3rhmetKqEtRxM4ZqP8uDaPje/HyNi2rNh9gsSMfJwdNQzr2ponv1pMQa0OzfXFw8WRGSN7GCT9LeDq5MCPz9zKu3+tZ+OhM8b3U6wO6dl6DyRZJq+4jD5R4bRt7s+k/p3q5MHo9BLzNx+26BGRZUMF1Lr98Yzt3aE+l2iTvh0irJagq0SBvh0jmvScl4rXflnD+gOnjWrHNe9ncXklz8xeyg/PTKFzy5Aru0iFa5IrasT4+/vj728+rn+j0MuvNSHOPmRU5JsV7RKA28L7ohYtN7xTuDTUJG/aU/3h6uTA6J7t7Uqo1UmyTQNGwHATVokGj6ReklCLIs9PHWyiEmuO8X068MPK3Rb3i6LAXUO78uj4vnX2fb1ku8311+Z0aja3DozhjovE2Ub3bM+8TYfq1Rahhpu6t+WNu0dabfJYg7e7C588MJaMvGJD+EWSef3XNXZ7Rmq0ZHaeSGLelsN8/vB4YttcCDvnFJZSUFJudQ61SuRkSlaTGzE39WjP18t2UlRWYVbMUJJlpg2zbORdLZxNy2GdhQRkWTZ4F79dvotvnpx8mVd29aCI3TWca8MXCSQnJ3Po0CGSk5PR6/UcOnSIQ4cOUVJydcTtG4pKEPlvt2l4aFwQajnhxep/DwnsyB0RdW82CpeObcfO8cBnf9P9sf/R/dH/MeOTeaw/eNpmRcrj4/vi7mLd8ycKAlFhdmiPCAL3jIhlxsge3NK/E8/dMoi1Hz7ILf072Tw02NeTh8b2NrtPJQoE+Xhwz4juxm1avR6tTs/e+BQSrCTKmmPprhMUl9X1uNwxpCvODpo6Krv2sGpvPM/MXkqODSXf2gT6uDO0S2tahfjVO7RTk1NZUanj8S8Xk5Z7oQDAwQ5DSpaxy+CqL65ODnzzxCQ8XJwQuBCiEwUBURR4Y9oIYiKDm/y8Tc3a/aesd+2WZHafTKaoEZ67ax65iV43INdMddJrr73GL7/8Yvy5S5cuAGzcuJFBgwZdoVU1DZFuzZjb70kWp+xjTfohSnSVtHD1Z1JYTwYGtFc0Yi4jP67aw1dLt5voiBxNSOf5s8uZMbJ7HfXX2ni7u/DDM7dy27u/WfS0SLLMPSO78+u6/WZl52sQBIMhUCNqZw+lFVWs3B3HlqMJVGp19I4K58z5HKOsv1olMjK2LU9PGoCXmzP/HjjFb//uN6rXujnXv8O2VqcnPjXbxHsBhpDW989M4blvl3E+twiVKFR3vbZv3l1xSdz733n88dIduLvYL4p3cWuG+iDJMlq9nr+3HOaJif0B8PFwoV1oAPGpWRbXrpck+kc3POxtjbahASx7+15W7o5j67FzaHV6OkQEMqlfNMG+5rRzrj6KyyqrvZk2dHkqqvCohwCiggJcQ0bMnDlzmDNnzpVexiXD28GNGS0HMaPloCu9lBuWE0kZfLXUEE6pbVzU/PvnNXvp2S7Mqh5M6xA/Prz/Zl78YQUCBqE7wNhL554RsQzs1BJPVydmfrrAEDYy890+bVi3ehkwp1Kzefh//xhDHzWhKL0kc//ongyIjiTU38uY//LVku38uHoPYq1QWUPF3UQL4ba2oQEseetedp9M5nhiBiqVwLxNh8gqsO1h0UsyqdmFvPnbOt6eMQpnB8vVWLUJ8nGnZZAvCRm5Dco5kiSZDYfOGI0YgHtHdeeF71eYHa8SBdo096drq0uXz+Hq5MCUgTFMGRhjdZxWryczrxiVSiTQ2/2qEcEL9feyqZDsqFHh4+5idcz1jBJOajjKI76CQjXzNx+26vZWiQLzNh+yOc+wrm2Y+8o0xvbugI+7Cx4ujvRqH84Xj0003hw7twxh1qMT8fc0dI2uud9o1Cruv6knj4+37PG5mPIqLY9+sZCisgoTr3JNPsoPK3dTXFZhNGAOn03jx9V7APuUd63h7KihfVgzi/tFUaB3VDj3j+5Jem6xXQZMbTYcOsMd7/5BdqHtsPGhs+d54fsVpOYUNKpQo+qiiqZhXdvw1KT+CGAMj9V8TloE+vC/RyZcUYNBq9Pz7fKdjHzpe8a99jM3v/IjE16fw6JtRy+LKJ8tRvdsj9pKFVVN124nh2vmmbrpUaqTGswN/KlRUDDlWGKG1URUvSRzPNE+XY7WIX68Pm2E1TG9o8JZ8e597IpLJiW7ADdnBwZGR9odPknOymfuxkOs3BNHUZllBV+Al35cyer3Z+LsoDEaaw1Juq2NIMBtgzpb1awBQ3LsjuPn+HvrkQadJzWngBe/X8FPz021OGb+5sN8MHeD2esyKCTbbjNQQ1REXaPs7uGxDOncikXbj5GYkVddhdWGfh1bmFUTbgwFJeVsOZpAcVkloQFe9ImKsHgOrV7PU18vYdfJJJN7WGp2AW//8S9JWfk8NWlAk66vvni6OvH8rYN4768NdcQgVaKAn6cbD40xn8OloGALxYhRUKjGQW37z8GeRM/6oBJF+naIqPdxO08k8dQ3S5AkyS5jpLisku+W7+TJSQOIS85slAFTYygM7NTSYgIxQG5RKR/P38S/B043yuOjl2QOnU0jLjnT6PXR6SU2HznLlqMJ5BaWseNEonHsxcgyDIqJ5Fhipl0endsGdTa7vbm/l9WcqMYiSTJfLd3O7//uR6uXjHlZvu4uvH73CPp1bFHnmBW74thpRmyv5l34dd1+RnRrS1S4ZW/Z5eCWATF4u7nwzfIdJKQbksfVKpFRsW15fGI/E72eGxElnNRwFCNGQaGaQTEtOZWabfGGqxIFBndu1eTnrajSkZZrkLIP8fO0GZooKq3g2W+XotPr6+VB/nvrUR4a2wfHBrjtRVGgbXN/JEkmxN+TiX2j6d0+3GL1UVFpBTM+mUd6blGjQ1ZgyLvZG59C+7BmpOUW8cisf0jOKsB2uqgBSZZxcdJAofVxHi5OdZKULxezFm/l13UXmnbWvG95JWU89c0Svn3yFrq1aW5yzPzNhy22uqhh3qZDvDl9pOUBl4mhXVszpEsrUrILKa+sItjXo15J29c1TVFdpBgxCgo3NpP6RfPrun1UVOnq3HhrFGmnDLBd4mwvpRVVfLNsB4u2HzMq/oYFeHH/TT0Z0yvK4nFLd52gsgFieaUVVZzPKWRI51acTs2x27gQMHgJJvTpaDO5tIY/NhwgLadpDJgaJFk2KArP+sco/2/v7HvjUxjfpwOp2QUWvVCiIHBzz3ZNtNr6kV1Ywu//HjC7r0bV+8ul2/n5opBaUla+zc/BhkNneOPuEVdFoq8gCIQFeF3pZShcRyiJvQoK1fh5uvLV45NwdXIw6HIIF15ODho+f2QCzf29muRc5ZVaZn66gLmbDpm0LEjJKuC1X9bw/cpdFo89kpDW4POqVSKT+kXj4qixWFV0MTX3yI/mbyS3yL7E3IU2mhfWF0mW6dwymM2Hz5KcZdkQsURFlY5bB3ZGMFFjuoCAwYi5dWBnu+aLS87k1TmrGfTsN/R/+isemfUPW48mNDiRtkbN1hKSLHP4bBoZecUm211s5COBwXjdG3/1tVxRuIDSO6nhKEaMgkItYloGs+Ld+3hh6mCGdG7F4JhWPDN5IKvevZ+eVkqr68tfGw8aQlcX3Yxrfvpm2U6SswrMHisKlm7FlhEwaLc09/PC18OVr5+cbBTmM8xnG1mGZTtP2BwnSTK5RWV2r00lCmisJMfWlDHHRAaz5WgCDdDPQ5JlQv29eP++0ahE0SQMJooCKlHk/ftGm/RMssTqvSeZ9sFfrN57kqKyCqOR8OTXS/jfoq0NMmTyS8qtVsbVUFhqqh5sTwdrQRBYsz++3mtSuIxIctO8bkCUcJKCwkW4OTsydVBnplpI8GwKrPXjAcONe8mOY2YTSXu0C2OtBRl3S8jAPSNijTfvjhGBrHzvflbvOcm+06lotXr+PXja6hyCAOcybSv6iqKAm5MDJRX26c50igzmxamD+e/fm9kbn2IiNCgKAj7uLnz8wBgEQWhQGA3ASaNGRmZo19YsCruHBVsOG70T3duGMmVATJ2+SeZIzyvi1TmrDeurtY4az9Cv6/bTpVUIAzu1rNf6gnw90Omta6kIAgR4uZlsu6V/J/7ccND65LLcYA0ghcuEkhPTYBQjRkHhMqPV68kqsF4lI8kyyVn5Zvfd1L0dXy7eRlFZpVXFX1m+UEk0bVg3Jl/UssDZQcPEftFM7BdNpVbH+kOnrRoIsgz74lN5+psl9Gwfzpie7XFzNt9mYWzvKOZvPmw17PPwmN4M69aGFoE+AHz5+ETW7I3n761HSM0uwMPVmTE92zOpXzRebs4AtGnub7EPjzUqtDpufuVH7h7ejdsGdWlw2fHCrUetvkeiIPDXxoP1NmKGd23Dh3M3Uqk13zJBJQr0j47E+yJBuPBm3ni4OFotsRdEgdAmCoMqKFxtKEaMgsJlRi2KNjtei4KAq5N5A8HZUcOXj0/k4VkLKSmvMoYvagyW0T3aUanVU1xWQXgzHyb160jbUOv9mhw1avp1bMGO44kWDQ9JlknPKyIjr4gtRxL4ZtkOvnp8Eh0jAuuMvWtYN1bsjqO0oqrOfKIg0LN9GPeP7mmSbKpRqRjTK8pqUvP4Ph34ZtmOBpWIZxWU8MmCzZxNy+U/dw5rUKLrkXPpVj1okixzNCG93vO6Ojnw/K2DeOePf+vsU4kCTg4anjDjlRMEgdsHd+G7FbssPojLksyEPk3bnFKhaRFoghLrJlnJtYdixCgoXGYEQWBkbFtW7omzeDPWSzIjYttYnCMqPJDFb85gyY5jbDp8liqtng4RzZgyIIY2zRvWGf6+UT3ZfjzR5riaFZeWV/HorIUsffteoxpwDUE+Hvz03FRe+mElZ9JyjKXQoiAwqns7XrlzaIOMCF8PV16/ewSvzVlT72NrWLT9GKN7tqdb6+a2B1+EPXkrohV1WmtM6heNq6MDXy7dbqy+AohtE8rztw4iotpjdTF3DevG+kNnSEjPNcmxqnnPHx7bp8kS0hUuEU2huKso9iooKFwupo+IZc2+eGRZqvNkLwoQFd6MXu3Crc7h7ebM1EGdGd61DW7OjnUMifri5KBGLYpUSZY9RLWRZJmSikqW7jzOtGHd6uyPDPJl3n/u4ui5DE6mZOGgVtE7Kpxm3u6NWueYnlHodHre+WN9gyqgVKLAwq1HG2TE9OnQgt0nky3eL1Si0CDxwhpGdm/LiNg2xKdmU1xWSYifp81Gj65ODvz4zBS+XraTxduPGUNSzf29uP+mHoztrXhhFK5fFCNGQeEKEBnky1dPTOLF71eQV1xmkswqyZBbVMaCLYeZMiDGrKBcTmEps5fvZPmuE8awVM92YTw4phedW9ZtRngyJYv5mw9z7Fw6GrWKQTEtmdg32qTJ5Ht/rkdno1HfxcgybDmSYNaIAYPXqVNkEJ0ig+o1rzUy8or5Z2vDS7j1ksy5DNsJyuYY1yuK71bspKxCa/b8kixz59CuDZq7BkEQaGcj/Hcx7i5OvDh1ME9M6EdqTiGOGhWh/l5XhTaMgm2ulGLvV199xccff0xGRgYxMTF88cUX9OjRw+zY77//nl9//ZVjx44B0K1bN9577z2L4y8XSom1gsIVolvr5qx87z66tg6pc0PMyCvmw3kbeffPf+uU7GYXlDDtgz9ZsuOYSV7N3vgU7v90AVuOJpiMn7N2L3e89wfLdh7nTFoucclZfLt8FxNe/5nDZw2aMwnpuYZ8jwbkmlTpzCejXgrKKqqY+el8TqZkNXgOQQB3F0eyC0r4ec0e3v9rPd8s22GXYePh6sSXj03CxUlDbftAJQqIgsDr00YQ3aLpDLb64uyooXWIH2EB3ooBcy0hN9GrHsybN49nnnmG119/nQMHDhATE8PIkSPJyjL/t7Vp0yZuv/12Nm7cyM6dOwkNDWXEiBGcP3++/tfbhAjy1dDm9DJRVFSEp6cnhYWFeHhYd9EqKFwOth5N4Mmvl1gd8/UTk+jV/kJo6ZWfVrF2f7zZfBpBMEjnr3l/Jg4aNTtOJPLYF4vMzisKAi5OGla8ez8HTqXy9Oyl9V6/ShSYOqgzz00ZVO9jrbHnZDJ/bDjA3vgUZBm6tArmzqHdSM0u4KN5GxtdTToopiVbjhiMPVEUkGUZvSQzrncUr9w5DI3Keo+sgpJylu06wbZj59DpJTpFBjG5X7SSe3KdcanvGTXz9xv8Bmp148LBOl0F2za+Yfdae/bsSffu3fnyyy8BkCSJ0NBQHn/8cV566SWbx+v1ery9vfnyyy+5++67G7X2xqCEkxQUriC2OkqrRIEFWw4bjZii0grW7j9lcbwsQ2FpBav3nWRc7478/u9+i/NLskxpeRUrdsfROtivQeuXZJlb+jddKwaA3//dz6f/bDFZ9974FHbFJdfRSTGHt5sz5ZVaqnT6Oh4ulSjg7uzIpsNnL1yD/sKYZTtP4OLowAtTB1s9h5ebM9OGdbMYRlNQqA+CLCM00p9Qc3xRUZHJdkdHRxwdTSsdq6qq2L9/Py+//LJxmyiKDBs2jJ07d9p1vrKyMrRaLT4+5hPOLxdKOElB4QpyNj3XarmwXpI5cz7X+PP53EL0duStfPDXRk4mZ7IvPtV6ObJgMBA6tQzCx93Z7nXXVOm8fNsQi1UzDeFkShaf/rMFMO1IXfPvrIISm14YB42KX168nSAfQwKxWhSN620V7IdgpcJIBhZsOULeRYrD5VVasgpKKK/Smj9QQaExSE30AkJDQ/H09DS+3n///Tqny8nJQa/X06yZaXfzZs2akZGRYdeSX3zxRYKDgxk2bFh9r7ZJUTwxCgpXEFdHB9tjnBzM/tsalTodj36xCEm2YfDIIMsyGpWKh8b05r2/NlgcGujjTk5hKSpRoGf7cKYN7Vanq3JjmbfpkFXPlC1EQSDU35vWIX4seetedp5I5NDZNERRoGe7MFSiyIxP5lmdQy9JbD12jvF9OpCclc+3y3exbv8pdJKEWhQZEduWB27upTQyVLgqSUlJMQknXeyFaQo++OAD5s6dy6ZNm3ByurKdyBUjRkHhCjIyti2zl++0qrxbuz9OqL8XkUE+nMvIs6mum19STliAF6nZhZYreQTo0spQzTS5fydKKqr4eukO9JKEShQNib4CTB8ey6Pj+pqtlGpKjiSkN9iAAUN4a3L/aMCQ69K3Ywv6dmxh3L/7ZLLNOQQByiurOH0+h3s/mUdFlda4Jp0ksWbfSbYcPctPz06lVUjDwnAKCrVpynCSh4eHzZwYPz8/VCoVmZmZJtszMzMJDKwrXlmbTz75hA8++IB///2XTp2aNpTcEJRwkoLCFWRy/2jcXRzNiqipRAFvN2cm9O1o3CYIAg+P6WOXrpUoCHi7uVg1kBzVasZWK+QKgsA9I7qz9oMHeP7Wwdw1tCtPTR7Aqvfu5/EJ/S65AQPgoLaeUAuGTtzmCm8EAfp2iGBY19YWjw0P8LapbCrL0CLIlzd/W0t5LQOmBr0kU1ap5a3f19lca32RJJmDZ86zZl88+06l2BU6VLgOuMzVSQ4ODnTr1o3169cbt0mSxPr16+ndu7fF4z766CPefvttVq9eTWxsbD0u8NKheGIUFK4g3u4ufPf0FJ78ajEZ+cWoVSLIhif+Zt7uzHp0Qh0Ru6FdW/PybYN5f+5Gq3PLyPh6uDBjZHd+XrPXJEyjEgVEUeSTh8Ya+xLV4OXmzG2XsPmlNfpHRxKfmm11jE4vMW1oVxbvOE5xuaFnkJuTA1MGxvDQmN6orCjmBvq407djC3aeMN9eQRQEAn3c8XRx4kRSppkZDEiSzLHEDE6fz6F1E3ljth07xwdzN5CWeyEx09/TlWduGWhXt2qFa5groNj7zDPPMH36dGJjY+nRoweff/45paWlzJgxA4C7776bkJAQY07Nhx9+yGuvvcaff/5JRESEMXfGzc0NNzfbCfeXCsWIUVC4wrQO8WPp2/ey9WgC+0+nIggCsa2b0y+6hcUb8pSBnfln2zFOp2ZbfQCLbhHE9BGx9GgXxrxNhziemGEUu7t1YOerLq9jQHQkP6zabXNcsJ8n6z58gLPpuciyQTzQycG+r7OXbhvM3R/OpbC03MSQUYkCKlHknRmjSLSjWzcY9HWawojZfuwcT369uM727MJSXv5xJXpJYnSP9o0+j4JCDVOnTiU7O5vXXnuNjIwMOnfuzOrVq43JvsnJySYtNL755huqqqq45ZZbTOZ5/fXXeeONNy7n0k1QjBgFhasAtUpkcOdWDO7cyu5jbu7Zns+seC00KhXjqhv/9WwXRs92YY1e56VGpbIdslKrRPJLynHQqGkf1szm+IsJ9vXk95fu4PuVu1ixO44qnR5REBjYqSUP3NyLNs392Vxy1vZEgIujpt7nvxhZlvl4wabqf5sf898FmxnerY1N/RqFa5Mrpdj72GOP8dhjj5ndt2nTJpOfExMT63+Cy4BixCgoXIOUVlTx+7/7rY55+bYheLvZXzZ9NRDg5WZsXGgJvV4i0Kdx/ZcCfdx59a7hvDB1MAUl5bg7O+JSq/Kre9tQnB00VkuqnR01xLYNbdQ6AE4kZZKcVWB1TH5JObvjkulXK0lZ4TpCaQDZYBQjRkHhGmTF7jhyCkst7hcESMrKJz4liyU7jpNZUIKPuzM392xPTGSwXZL0ccmZzNt0iH2nDCGu3u3DmToohpYNFMazBy83Z9qHNyMuOdPid7KDRs2wLpaTd+uDo0ZttiGli5MD00fEMnu5ZeGvGSO64+zQeE9MtpXfo8m4gpJGn0tB4XpDMWIUFK5B/j1wyup+WYZ5mw8zZ+0+RMHQVFIU4J+tRxnYKZIP7r8ZR43hz79Kq2Pb8USy8ovx8XChf3QkK3ad4P2/NiDWSgZetP0oC7cf5Z17RjGqe7smv6bdJ5N5bc5qmzf1pycPwM256bUvLub+m3pSXF7Jn+sPIFT3RpJkGVmSuWNoV+4d1TSN72o34WyKcQrXHoJkeDV2jhsRxYhRULgGKSmvsllRWV5pCIXU5K7W/H/LkQQ+mreRV+8azvLdJ/hk/iaKyioRBIPx4+SgpqLK0NTRnGruf+asJiq8GWEB3k12PccSM3j8y0VWS4qbebvx6Pi+DIyOJL+kHC9Xp0va5FAUBZ69ZSC3Deps9Hz5ebpyc8/2hPh5Ntl5OoQ3I9Tfi9ScAoveJy83Z3pFhZvfqXDto4STGoxixCgoXIO0DvHj9PnsBgnDycCSHcfoEN6Md/68oBNR8x1YY8BYQsAgzf/sLQPrfW5LzF62w+DlsHI5dw+L5a/1B3ltzhoAAr3duWNIF24b3MVQmn6JCPHz5IGbe12y+QVB4PlbB/Hk14st5gM9c8sAJalXQcEMitidgsI1yKT+0Y1UtoUvlmxv0LF6SWaPHcq39lJYWsHOE0kGdWALCAJ8vGCTiYZMRn4xn/2zhRe+W37Ni8L169iCTx8aVydh2dfDhXdmjGJMz6grtDKFy8JlFru7nlA8MQoK1yAxkcHcNbQrv68/YLOaxxKFpRUNPn9ThnGKyypsrr/GQ3Ox+rAMbDpylpW74xjbu0OTrelKMLBTS/p3jOTgmfNkFhTj6+5Ctzahl9TLpHB10JRtB240FCNGQeEa5enJA4gM9uWXtftIyswHDCXKKlEgPa/4kp1XVd1Msanw9XBFoxLR6hvmTREFgXmbD1/zRgwY8nCauqmmgsL1jGLEKChcowiCwIQ+HRnfuwO5RWVIsoyfhysv/rDCphFTU7FU73MCAgJTBjRd4zdnRw2jurdj5Z64BoXIJFkmIT23ydajoHDZURJ7G4zip1RQuMYRBAE/T1cCvNwQRYGRsW1sHnNTj/a0CvY120ixNrV7PtbI8r9//2ia+3s1btEX8dDY3ni6OplthGkP1volKShc9ciA1MjXjWnDKJ4YBYXrjYExLWnT3J8zaTl1kmUFDJ6Ph8f2IT2viIc//wcJ2STXpMaweeHWwSSk57HvVAqCAL2jIpgyIOaS9FsK8vHg1xdv55MFm9l85KzxoTLI1wNHjYrEjHyrx3u5OVndr6BwNaPkxDQcxYhRULjO0KhUfPPEJJ7/fjkHTp9HFAUEBPSSQa7/kwfHEuzrQbCvB98+fQufLNhEXHKW8fgWgT48PXkgfTtEXNZ1B/t68ulD48guLCElqwBXJwdah/hz27u/2zy2tNxyewAFBYXrF8WIUVC4DvF2d+GHZ24lLjmTHceT0On1dIwIpHdUBGKtkE2XViH88fKdJKTnklVQgo+7C61D/C6piJwt/D3d8Pd0M/7soLatj2JvB2sFhasSmSbIiWmSlVxzKH/5ZtDr9Wi1ypOdQtOj0WhQXUbRsvZhzezq9BwZ5EtkkO9lWFH96RfdgpMpWXXKq2tQiQIDOkUaf84pLGVPfDJanURUeDNah1y6Xk8KCk2CktjbYBQjphayLJORkUFBQcGVXorCdYyXlxeBgYFX1NtxLTG5XzS/rttHlVZfx5CpqZaaOqgz5VVaPpy7geW74kzGxUQG886MUU3aKkBBQeHqQDFialFjwAQEBODi4qLcZBSaFFmWKSsrIyvLkH8SFBR0hVd0beDv5cYXj07kia8WU6HVIVcbKKIgoBIFPpw5hvAAbx7/chG7TybXMXSOJaZzz8dzmfvKXfh6KE0UFa5CJAwWeWPnuAFRjJhq9Hq90YDx9b063eoK1z7Ozs4AZGVlERAQcFlDS1cCWZY5lZpNQWkFQT4eDa5s6tamOcvfvY+lO46zJz4ZSZLp0iqEiX074u/lxq64JHbGJZk9Vi/J5JeU89fGQzw2vm8jrkZB4dKgVCc1HMWIqaYmB8bFxeUKr0TheqfmM6bVaq9rI2bT4bN8vnALyVkFxm2dWwbzwtTBtAsNqPd83m7OTB8Ry/QRsXX2rdwdh0oULIrlSZLMkh3HFCNGQeE6Q1GIugglhKRwqbkRPmOr957kmdlLSallwAAcOZfOjI/ncTIly/yBDSSnqNSm2m9BSXmTnlNBocmoSext7OsGRDFiFK5a3njjDTp37nyll1FvdHqJ0ooqyiqqrHZmvl6p1Op4f+4GoG7VpyTJVGp1vDZnTZO+N8283W2q/fp5KvkwClcpihHTYBQj5hrnnnvuQRAEBEFAo9HQrFkzhg8fzk8//YQk1S/Ta86cOXh5eV2ahTaA5557jvXr19frmIiICD7//PNLsyAb6PQS53MKOZWaTVJmPomZ+Zw6n012YYkxGfVGYOvRBIrLKq2OOZOWw9R3fyOvqKxJzjmudwernhhREJjUL7pJzqWgoHD1oBgxTYxekjiVms3xxAyKy61/kTcVo0aNIj09ncTERFatWsXgwYN58sknGTNmDDqd7rKs4VLg5uZ2zSRZ6yWJpMw8CksrTLZLkkx2QSnpeUU3jCGTnldsIqhniXPpeTz1zZImeV86twxmRLc2Zgs8VKJAsJ8HUwd2bvR5FBQuCYonpsEoRkwTIcsy8zYd4uZXfuS2d39n2od/MeyFb3n793UUXXRja2ocHR0JDAwkJCSErl278n//938sWbKEVatWMWfOHOO4Tz/9lOjoaFxdXQkNDeWRRx6hpKQEgE2bNjFjxgwKCwuNnp033ngDgN9++43Y2Fjc3d0JDAzkjjvuMJYJWyIiIoK3336b22+/HVdXV0JCQvjqq69MxiQnJzN+/Hjc3Nzw8PDg1ltvJTMz07j/4nDSPffcw4QJE/jkk08ICgrC19eXRx991JiUPWjQIJKSknj66aeN1wCQlJTE2LFj8fb2xtXVlQ4dOrBy5cqGvt1myS8up1Krt7i/oKSCiqpr16CsD16uTnaFiiRZ5lhiBgfOnG/0OQVB4O0Zo5g+ItZEvVcQYECnSH5+bioerkp/JYWrlMY2f6x53YAoRkwTMWvxNj6ct5GsghLjNq1Oz9Kdx7n3v/MouUxemRqGDBlCTEwMCxcuNG4TRZFZs2Zx/PhxfvnlFzZs2MALL7wAQJ8+ffj888/x8PAgPT2d9PR0nnvuOcN1aLW8/fbbHD58mMWLF5OYmMg999xjcw0ff/wxMTExHDx4kJdeeoknn3ySdevWASBJEuPHjycvL4/Nmzezbt06EhISmDp1qtU5N27cyNmzZ9m4cSO//PILc+bMMRpqCxcupHnz5rz11lvGawB49NFHqaysZMuWLRw9epQPP/wQNzc3K2epP/kltsMi+TdIYumgmJZ2tQoAQ/fpTYfPNsl5NSoVT0zsz7oPH+TLxyfy2cPjWPnu/fz3wXGKPozCVU1NiXVjXzciSol1E5CQnssva/eZ3aeXZBIz8vlr40Fmju51WdfVrl07jhw5Yvz5qaeeMv47IiKCd955h4ceeoivv/4aBwcHPD09EQSBwMBAk3nuvfde478jIyOZNWsW3bt3p6SkxKox0LdvX1566SUA2rRpw/bt2/nss88YPnw469ev5+jRo5w7d47Q0FAAfv31Vzp06MDevXvp3r272Tm9vb358ssvUalUtGvXjptvvpn169czc+ZMfHx8UKlURo9RDcnJyUyePJno6GjjNTQ1Op3txyCtzrKn5nrC3cWJ+0f35OulO2yOFQSo0jath8rVyYE+URFNOqeCgsLVieKJaQKW7DhutTJCkmUWbDlicf+lQpZlk3Lef//9l6FDhxISEoK7uzvTpk0jNzeXsjLrXoT9+/czduxYwsLCcHd3Z+DAgYDBOLBG79696/wcFxcHQFxcHKGhoUYDBiAqKgovLy/jGHN06NDBRFslKCjIZmjriSee4J133qFv3768/vrrJoZdUyGqbOeAqFU3zp/bfaN68Mi4PjbH6fSS0ttIQUHJiWkwN8636iUkNafQZg5ATmEpOv3lDVrGxcXRokULABITExkzZgydOnXin3/+Yf/+/cYclaqqKotzlJaWMnLkSDw8PPjjjz/Yu3cvixYtsnncpUKj0Zj8LAiCzSqs+++/n4SEBKZNm8bRo0eJjY3liy++aNJ1ebk62xzjeQPlZAiCwP039eSeEbFYksURMHSfvqlH+8u6NgWFqw5JbprXDYhixDQBHi6ONqsxnBzUNnUsmpINGzZw9OhRJk+eDBi8KZIk8d///pdevXrRpk0b0tLSTI5xcHBArzcNeZw8eZLc3Fw++OAD+vfvT7t27Wx6PmrYtWtXnZ/btzfcsNq3b09KSgopKSnG/SdOnKCgoICoqKh6X6+1awAIDQ3loYceYuHChTz77LN8//33DT6HOXzcXaz+fl0cNbg6OTTpOa8FZt7ci44RgYgXWTIqUUAQBd6dcdMN+b4oKCg0DYoR0wSMim1rVaNCJQqM7tH+kim1VlZWkpGRwfnz5zlw4ADvvfce48ePZ8yYMdx9990AtGrVCq1WyxdffEFCQgK//fYbs2fPNpknIiKCkpIS1q9fT05ODmVlZYSFheHg4GA8bunSpbz99tt2rWv79u189NFHnDp1iq+++ooFCxbw5JNPAjBs2DCio6O58847OXDgAHv27OHuu+9m4MCBxMbWlZW3l4iICLZs2cL58+fJyckBDLlAa9as4dy5cxw4cICNGzcajammQqNWERHog6OmbpqZu4sjoQFeN4RS78U4O2iY/dQtPDKuD/7VYnOiINA/2lAxNLhzqyu8QgWFqwAlnNRglMTeJqB72zC6tgrhUEJanbCSKAg4atTcPbzhN2ZbrF69mqCgINRqNd7e3sTExDBr1iymT5+OKBrs1JiYGD799FM+/PBDXn75ZQYMGMD7779vNHLAUKH00EMPMXXqVHJzc3n99dd54403mDNnDv/3f//HrFmz6Nq1K5988gnjxo2zua5nn32Wffv28eabb+Lh4cGnn37KyJEjAUO4YcmSJTz++OMMGDAAURQZNWpUo8M8b731Fg8++CAtW7aksrISWZbR6/U8+uijpKam4uHhwahRo/jss88adR5zOGrURAb5UF6lpbxShyCAm5MDDmYMmxsJZwcN947qwYyR3Smv1KLRqNBcxz2jFBTqT1MYITemESPIN4oCF1BUVISnpyeFhYV4eHiY7KuoqODcuXO0aNECJ6f65y6UlFfy6pzVbD6SgCgICML/t3fvQVEd+R7AvzMoAqOCIPLYIIjojRsVFcVAjIoSIFmJaEIS70aRKFkNYliSUhOzPGKMZdTo+laswtUbryZX0VvGZ1DiI2gEhRUjRBCURR66GpCHAjN9/+AycWRAYGY4DHw/VaeK6dOn53cO1MyPPn26659McrDuha/CpuAFF/tnN9KJuLi4IDIyUuOJKKqn698aEbWP5r4z9Nm+r+tCdJP30KmtOtVj/HBzvcFi7ai69r+IetTTvAfWzp+K/OL7OJeZh5o6Jf7DqR+8hji3aPZSIiLqovRxO6jr9EdoYBKjZy721nCxt5Y6DCIiMhYqAZ1vB3XRp5OYxJBB5OfnSx0CERF1ckxiiIiIpCRU9ZuubXRBTGKIiIikxDExbcYkhoiISEocE9NmnOyOiIiIjBJ7YoiIiKTE20ltxiSGiIhISgJ6SGL0EonR4e0kXVVXG7a+ARQXF+OVV16BQqGAlZWV1OGoyWQyHDx4UOowiIjISDCJ0UV8PDB8OPDESszNKiior6/HFZRnz56NoKCgVh2zdu1aFBUVIT09Hb/++qveYiEiojbgApBtxiSmraqrga++AnJygIkTn53IFBTU18vJqT9Owh6Z3NxceHh4YNCgQejXr1+b2qipqdFaXltbq0toRERdj0qln60LYhLTVubmwKlTgKsrcPNm84lMQwJz82Z9/VOn6o83gIkTJ2LhwoVYtGgRrK2tYW9vj9jYWPV+FxcX7N+/H7t27YJMJsPs2bMBAL/99hvmzp0LW1tb9O7dG5MmTUJGRob6uNjYWIwYMQI7duzQWLhQJpNhy5YteP3116FQKLB8+XIAwKFDhzBq1CiYmZnB1dUVcXFxqKurU7d348YNjB8/HmZmZvjjH/+IkydPtujcIiIiEBkZiT59+sDOzg7x8fGorKxEaGgoevXqBTc3Nxw9elTjuMzMTLz66qvo2bMn7OzsMHPmTNy7d0+9/9ixYxg3bhysrKxgY2ODKVOmIDc3V70/Pz8fMpkMBw4cgI+PDywsLODu7o6UlBR1nVu3biEwMBB9+vSBQqHACy+8gCNHjrTgN0ZERG3FJEYXTk5AcnLziczTCUxycv1xBvSPf/wDCoUCFy9exFdffYXPP/9cnSRcunQJAQEBeOutt1BUVIS///3vAIDg4GCUlpbi6NGjSEtLw6hRozB58mTcv39f3W5OTg7279+PAwcOID09XV0eGxuLadOm4erVq3jvvfdw9uxZzJo1Cx9++CF++eUXbNu2DTt37lQnOCqVCtOnT4epqSkuXryIrVu3YvHixS0+t759++Lnn39GREQE5s+fj+DgYHh7e+Py5cvw8/PDzJkzUVVVBaA+OZs0aRJGjhyJ1NRUHDt2DCUlJXjrrbfUbVZWViIqKgqpqalISkqCXC7HtGnToHrqP5ulS5fi448/Rnp6OgYPHowZM2aoE7Pw8HA8fvwYZ86cwdWrV7Fy5Ur07Nmzlb85IuqSeDup7UQXUlZWJgCIsrKyRvuqq6vFL7/8Iqqrq1vf8O3bQri61v8ZubrWv26uXI9CQkLE1KlT1a8nTJggxo0bp1FnzJgxYvHixerXU6dOFSEhIerXZ8+eFb179xaPHj3SOG7gwIFi27ZtQgghYmJiRPfu3UVpaalGHQAiMjJSo2zy5Mniyy+/1CjbvXu3cHBwEEIIcfz4cdGtWzdRWFio3n/06FEBQCQmJjZ5rk+fW11dnVAoFGLmzJnqsqKiIgFApKSkCCGEWLZsmfDz89Nop6CgQAAQ2dnZWt/n7t27AoC4evWqEEKIvLw8AUDs2LFDXefatWsCgLh+/boQQohhw4aJ2NjYJmN/kk5/a0as5MFDsT7xrAj4ZLsYF7lR/OeX/yUSz18VNXV1UodGpFVz3xn6bN+373sioN88nTbfvu8ZNNaOio9Y60NDj0xDj8vEicDu3cDMme3aA9Ng+PDhGq8dHBxQWlraZP2MjAxUVFTAxsZGo7y6ulrjtoqzszNsbW0bHT969OhG7Z0/f17d8wIASqUSjx49QlVVFa5fvw4nJyc4Ojqq93t5ebX63ExMTGBjY4Nhw4apy+zs7ABAfb4ZGRk4ffq01l6R3NxcDB48GDdu3EB0dDQuXryIe/fuqXtgbt++jaFDh2p9bwcHB/X7PP/881i4cCHmz5+PEydOwNfXF2+88Uaj30NX9uu/7iJs7XeofFQD1f/PLJpdUIrPd5/EsUtZWP9BEEy78+OIiFrHKD418vPzsWzZMpw6dQrFxcVwdHTEu+++i6VLl8LU1FTq8Oo9nci89FJ9eTsnMADQvXt3jdcymazRrZEnVVRUwMHBAcnJyY32PfkItkKh0Hr80+UVFRWIi4vD9OnTG9VtGEvTVtrO7ckymUwGAOrzraioQGBgIFauXNmorYZEJDAwEM7OzoiPj4ejoyNUKhWGDh3aaPByc+8zd+5c+Pv74/vvv8eJEyewYsUKrFmzBhERETqdb2egVKkQtfV/NRIY4PdZ0i9l/wvxRy4ifOpLEkVIJDEuO9BmRpHEZGVlQaVSYdu2bXBzc0NmZibCwsJQWVmJ1atXSx3e75yc6ntgXnriw3j37nZNYNpi1KhRKC4uRrdu3eDi4qKX9rKzs+Hm5qZ1/5AhQ1BQUICioiJ1InHhwgWd37epWPbv3w8XFxd069b4z/3f//43srOzER8fj5dffhkAcO7cuTa9l5OTE+bNm4d58+bhk08+QXx8PJMYACm/3MKdf5c3uV8IgW9/zEDYa2PZG0NdkhAqCB1Xodb1eGNlFAN7AwICkJCQAD8/P7i6uuL111/Hxx9/jAMHDkgdmqaCgvpbSE+aObPl88hIxNfXF15eXggKCsKJEyeQn5+Pn376CUuXLkVqamqr24uOjsauXbsQFxeHa9eu4fr169i7dy8+++wz9fsNHjwYISEhyMjIwNmzZ7F06VJ9nxaA+gG39+/fx4wZM3Dp0iXk5ubi+PHjCA0NhVKpRJ8+fWBjY4Pt27cjJycHp06dQlRUVKvfJzIyEsePH0deXh4uX76M06dPY8iQIQY4I+NzNa8IJvLmP2oeVj/Gv+6VtVNERB2MEPU9KbpsXXRgr1EkMdqUlZXB2tq62TqPHz9GeXm5xmYwTz+FdP58yx6/7gBkMhmOHDmC8ePHIzQ0FIMHD8Y777yDW7duqceYtIa/vz8OHz6MEydOYMyYMXjxxRexdu1aODs7AwDkcjkSExNRXV0NT09PzJ07V2P8jD45Ojri/PnzUCqV8PPzw7BhwxAZGQkrKyvI5XLI5XLs3bsXaWlpGDp0KP76179i1apVrX4fpVKJ8PBwDBkyBAEBARg8eDA2b95sgDMyPnKZDC3pKn9WokNE9DSZEMaXvuXk5MDDwwOrV69GWFhYk/ViY2MRFxfXqLysrAy9e/fWKHv06BHy8vI05kBpsaYeo5bg8Wrq+HT6WzNCGbl3ELp6X7N1+ln1xPfL5zCRoQ6lvLwclpaWWr8z9Nn+ZMuZ6CbTbXxnnahBUtlug8XaUUn6ibFkyRLIZLJmt6ysLI1jCgsLERAQgODg4GYTGAD45JNPUFZWpt4KDNEb0lyi0pJ5ZIg6ueGuDnjB2Q4mclmTdUJeGc0EhrouztjbZpKOovvoo4/UM8Y2xdXVVf3znTt34OPjA29vb2zfvv2Z7ffo0QM9evTQNcymtaSnRdvj1+yRoS5EJpPh63mv4/21/4NbpQ8gl8mgEgImchmUKoE3Xx6Od3xGSB0mERkhSZMYW1tbrfOOaFNYWAgfHx94eHggISEBcqn/a6uuBiZNatmtoqcTmUmTgH/+02BLDxB1NLZWPfHfS9/FybRsHE/9FeVVj+Bi3wdvjBsO94GOz26AqDMTenjE2vhGhuiFUTzPWFhYiIkTJ8LZ2RmrV6/G3bt31fvs7e2lCcrcHFi0qH4xx1Onnt2z0pDITJpUfxwTGOpizEy7IdDrBQR6vSB1KEQdilCpIGR8xLotjCKJOXnyJHJycpCTk4PnnntOY5+k45LDwoB33215QuLkxB4YIiIiPTGKkXSzZ8+GEELrpm+tbrO1CQkTmC7PCB8IJCJD4gKQbWYUPTHtoWFK+aqqKpgz0SADalhh++klFIioi1IJQMYxMW3BJOb/mZiYwMrKSr1woIWFhXp9HCJ9EEKgqqoKpaWlsLKygomJidQhEREZNSYxT2gYJNzcis9EurKyspJuQDoRdTxCANBxYC57Ykgmk8HBwQH9+vVDbW2t1OFQJ9S9e3f2wBCRBqESEDreTuqqY+2YxGhhYmLCLxoiImofQgXde2Jaf/ymTZuwatUqFBcXw93dHRs2bICnp2eT9b/77jv87W9/Q35+PgYNGoSVK1fitdde0yVqnRnF00lERESkP/v27UNUVBRiYmJw+fJluLu7w9/fv8nhFD/99BNmzJiBOXPm4MqVKwgKCkJQUBAyMzPbOXJNRrkAZFsZejEvIiLqPNprAciJsmnoJtPtacU6UYtkkdjiWMeOHYsxY8Zg48aNAACVSgUnJydERERgyZIljeq//fbbqKysxOHDh9VlL774IkaMGIGtW7fqFLsu2BNDREQkJaHSz9ZCNTU1SEtLg6+vr7pMLpfD19cXKSkpWo9JSUnRqA8A/v7+TdZvL11qTExDp1N5ebnEkRARUUfX8F1h6BsWdajVeemkOtQ/jPL095u2hZDv3bsHpVIJOzs7jXI7OztkZWVpbb+4uFhr/eLiYt0C11GXSmIePnwIAHDiCtJERNRCDx8+hKWlpd7bNTU1hb29Pc4VH9FLez179mz0/RYTE4PY2Fi9tN8RdakkxtHREQUFBejVq1eHm8iuvLwcTk5OKCgo4HgdA+D1NSxeX8PjNTYsbddXCIGHDx/C0dEwK62bmZkhLy8PNTU1emlPCNHou+3pXhgA6Nu3L0xMTFBSUqJRXlJS0uQcVvb29q2q3166VBIjl8sbLSDZ0fTu3ZsfUAbE62tYvL6Gx2tsWE9fX0P0wDzJzMwMZmZmBn2Pp5mamsLDwwNJSUkICgoCUD+wNykpCQsWLNB6jJeXF5KSkhAZGakuO3nyJLy8vNoh4qZ1qSSGiIiIgKioKISEhGD06NHw9PTEunXrUFlZidDQUADArFmz8Ic//AErVqwAAHz44YeYMGEC1qxZgz/96U/Yu3cvUlNTsX37dilPg0kMERFRV/P222/j7t27iI6ORnFxMUaMGIFjx46pB+/evn0bcvnvDzB7e3tjz549+Oyzz/Dpp59i0KBBOHjwIIYOHSrVKQBgEtNh9OjRAzExMVrvX5LueH0Ni9fX8HiNDasrXt8FCxY0efsoOTm5UVlwcDCCg4MNHFXrdKnJ7oiIiKjz4GR3REREZJSYxBAREZFRYhJDRERERolJDBERERklJjEdTH5+PubMmYMBAwbA3NwcAwcORExMjN5mdCRg+fLl8Pb2hoWFBaysrKQOp1PYtGkTXFxcYGZmhrFjx+Lnn3+WOqRO48yZMwgMDISjoyNkMhkOHjwodUidxooVKzBmzBj06tUL/fr1Q1BQELKzs6UOi1qBSUwHk5WVBZVKhW3btuHatWtYu3Yttm7dik8//VTq0DqNmpoaBAcHY/78+VKH0ins27cPUVFRiImJweXLl+Hu7g5/f3+UlpZKHVqnUFlZCXd3d2zatEnqUDqdH3/8EeHh4bhw4QJOnjyJ2tpa+Pn5obKyUurQqIX4iLURWLVqFbZs2YKbN29KHUqnsnPnTkRGRuK3336TOhSjNnbsWIwZMwYbN24EUD99uZOTEyIiIrBkyRKJo+tcZDIZEhMT1VPFk37dvXsX/fr1w48//ojx48dLHQ61AHtijEBZWRmsra2lDoOokZqaGqSlpcHX11ddJpfL4evri5SUFAkjI2q9srIyAODnrRFhEtPB5eTkYMOGDfjLX/4idShEjdy7dw9KpVI9VXkDOzs7FBcXSxQVUeupVCpERkbipZdeknwqfWo5JjHtZMmSJZDJZM1uWVlZGscUFhYiICAAwcHBCAsLkyhy49CW60tE1CA8PByZmZnYu3ev1KFQK3DtpHby0UcfYfbs2c3WcXV1Vf98584d+Pj4wNvbW/JVQo1Ba68v6Uffvn1hYmKCkpISjfKSkhLY29tLFBVR6yxYsACHDx/GmTNn8Nxzz0kdDrUCk5h2YmtrC1tb2xbVLSwshI+PDzw8PJCQkKCxkihp15rrS/pjamoKDw8PJCUlqQebqlQqJCUlNbmwHFFHIYRAREQEEhMTkZycjAEDBkgdErUSk5gOprCwEBMnToSzszNWr16Nu3fvqvfxP1v9uH37Nu7fv4/bt29DqVQiPT0dAODm5oaePXtKG5wRioqKQkhICEaPHg1PT0+sW7cOlZWVCA0NlTq0TqGiogI5OTnq13l5eUhPT4e1tTX69+8vYWTGLzw8HHv27MGhQ4fQq1cv9TguS0tLmJubSxwdtYigDiUhIUEA0LqRfoSEhGi9vqdPn5Y6NKO1YcMG0b9/f2Fqaio8PT3FhQsXpA6p0zh9+rTWv9eQkBCpQzN6TX3WJiQkSB0atRDniSEiIiKjxMEWREREZJSYxBAREZFRYhJDRERERolJDBERERklJjFERERklJjEEBERkVFiEkNERERGiUkMERERGSUmMUQdwOzZs7WuvP3kdPO62LlzJ6ysrPTSVludOXMGgYGBcHR0hEwmw8GDByWNh4iMH5MYog4iICAARUVFGltHXJCutra2TcdVVlbC3d0dmzZt0nNERNRVMYkh6iB69OgBe3t7jc3ExAQAcOjQIYwaNQpmZmZwdXVFXFwc6urq1Md+/fXXGDZsGBQKBZycnPDBBx+goqICAJCcnIzQ0FCUlZWpe3hiY2MBQGuPiJWVFXbu3AkAyM/Ph0wmw759+zBhwgSYmZnhm2++AQDs2LEDQ4YMgZmZGZ5//nls3ry52fN79dVX8cUXX2DatGl6uFpERFzFmqjDO3v2LGbNmoX169fj5ZdfRm5uLt5//30AQExMDABALpdj/fr1GDBgAG7evIkPPvgAixYtwubNm+Ht7Y1169YhOjoa2dnZANDq1bqXLFmCNWvWYOTIkepEJjo6Ghs3bsTIkSNx5coVhIWFQaFQICQkRL8XgIioKVKvQElE9Strm5iYCIVCod7efPNNIYQQkydPFl9++aVG/d27dwsHB4cm2/vuu++EjY2N+nVCQoKwtLRsVA+ASExM1CiztLRUr+Kbl5cnAIh169Zp1Bk4cKDYs2ePRtmyZcuEl5fXs061yfclImot9sQQdRA+Pj7YsmWL+rVCoQAAZGRk4Pz581i+fLl6n1KpxKNHj1BVVQULCwv88MMPWLFiBbKyslBeXo66ujqN/boaPXq0+ufKykrk5uZizpw5CAsLU5fX1dXB0tJS5/ciImopJjFEHYRCoYCbm1uj8oqKCsTFxWH69OmN9pmZmSE/Px9TpkzB/PnzsXz5clhbW+PcuXOYM2cOampqmk1iZDIZhBAaZdoG7jYkVA3xAEB8fDzGjh2rUa9hDA8RUXtgEkPUwY0aNQrZ2dlaExwASEtLg0qlwpo1ayCX14/V//bbbzXqmJqaQqlUNjrW1tYWRUVF6tc3btxAVVVVs/HY2dnB0dERN2/exJ///OfWng4Rkd4wiSHq4KKjozFlyhT0798fb775JuRyOTIyMpCZmYkvvvgCbm5uqK2txYYNGxAYGIjz589j69atGm24uLigoqICSUlJcHd3h4WFBSwsLDBp0iRs3LgRXl5eUCqVWLx4Mbp37/7MmOLi4rBw4UJYWloiICAAjx8/RmpqKh48eICoqCitx1RUVGjMe5OXl4f09HRYW1ujf//+ul0kIuqapB6UQ0T1A3unTp3a5P5jx44Jb29vYW5uLnr37i08PT3F9u3b1fu//vpr4eDgIMzNzYW/v7/YtWuXACAePHigrjNv3jxhY2MjAIiYmBghhBCFhYXCz89PKBQKMWjQIHHkyBGtA3uvXLnSKKZvvvlGjBgxQpiamoo+ffqI8ePHiwMHDjR5DqdPnxYAGm0hISGtuFJERL+TCfHUDXEiIiIiI8DJ7oiIiMgoMYkhIiIio8QkhoiIiIwSkxgiIiIySkxiiIiIyCgxiSEiIiKjxCSGiIiIjBKTGCIiIjJKTGKIiIjIKDGJISIiIqPEJIaIiIiMEpMYIiIiMkr/B1ySLsoZUU4XAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(data[:, 0], data[:, 1], c=latent.assignments, cmap='viridis', alpha=1.0, label='Data points')\n",
+    "plt.scatter(latent.mu[:, 0], latent.mu[:, 1], c='red', marker='x', s=100, label='Inferred means')\n",
+    "plt.xlabel('Feature 1')\n",
+    "plt.ylabel('Feature 2')\n",
+    "plt.title('Scatter plot of data with inferred latents')\n",
+    "plt.colorbar(label='Cluster')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/tutorials/getting_started.md b/docs/tutorials/getting_started.md
deleted file mode 100644
index 2de0565..0000000
--- a/docs/tutorials/getting_started.md
+++ /dev/null
@@ -1,16 +0,0 @@
-<!-- [TOC] -->
-
-This tutorial introduces Dirichlet Process Mixture Models and explores how to cluster using a simple example.
-
-# Dataset
-First we need a dataset. In this tutorial, we will synthetically generate points on on the 2D plane and form small clusters that we expect to later detect during inference. We will generate data using a [Gaussian Mixture Model](https://en.wikipedia.org/wiki/Mixture_model#Gaussian_mixture_model) in pure JAX and later feed in this dataset to GenJAXMix.
-
-# Generative Model
-
-# Inference 
-
-## Inferring Cluster Means
-
-## Inferring Cluster Weights
-
-## Inferring Data Point Membership
diff --git a/docs/tutorials/test.py b/docs/tutorials/getting_started.py
similarity index 100%
rename from docs/tutorials/test.py
rename to docs/tutorials/getting_started.py
diff --git a/docs/tutorials/heart_disease.md b/docs/tutorials/heart_disease.md
deleted file mode 100644
index ab405f5..0000000
--- a/docs/tutorials/heart_disease.md
+++ /dev/null
@@ -1 +0,0 @@
-Something here...
\ No newline at end of file
diff --git a/docs/tutorials/inferred_clusters_animation.mp4 b/docs/tutorials/inferred_clusters_animation.mp4
new file mode 100644
index 0000000000000000000000000000000000000000..4618aa00735e093d74351a7d89c0c3d86556483e
GIT binary patch
literal 30430
zcmX_m18^o?&~BWKZN0H=JK0zp+qP}nwryJ*ZEV}Nlbi4V@4ZzsbDq=vbf50iQ!`aF
z4Fm)PFmZCXGk3JL1_A;B`fvO>ne<%@8LjPD7=eI*ewjEJ8v_A3LRlN>JN<BKAi%%B
zD>g(=yN_0Xx2Djn09J{wuHBf}SOByDLt6)90OJpIU}j|C05CHeGjf`6{wRLa{SfG7
z<VD44Spb46!aqzSW5XYXu&tfDm9dEvfQf;DnU;xxiTOup=Hz6@MNjYQ>PqKgZe(n0
zrEf!L>tIU%-zs!wPS#dG99uglb6XonE`XuFfxaOxBf!DfgqIm$WNct%YiP;K$i=|L
z0MNJ5w{mwh=4EhW=3;PTWMl?d8}phOy8#@X4So<iz|PVA$Mol_>tMvoNXPJF^m74N
zo4Xks>Hg=D@y9~fLEpyIn3s_aU})xGYpt*QW6B6{axk{CGI#tz9Bv#&hE6}g(B7Jt
z;U@%rBM(~}V_qgk8b(HdiN2$guAQT$x!r$^|8-z*r)z6s;%Mx|OUndsGIRK`_;JJp
zu(Gwa)HnN?>Ha?<6Ts2R-0&xu|0iGo*f{*pB8KMH`cD4^Vs7JP>|mw;L;8^$SUEfB
zyXzX-THEP6{mc!2_Q=UW-`wWM#ShUz|38k2gTA$~<Iipx=-RpeU~?m0rXQ?tq;L0M
z8V0%s=K7BREn@Cq{6C4g8k?J%IT`%uZ0(F~bWLsTezgB9wEHo&G<N^-&CA5f@PDDM
zwYd#1E5Omv*v8n<*@>5h;lC&y^#9vZ2V+OGA8Q9g-T$BMe|iT)UPA{HfVIKTzW$fh
z&xDtWnT`Qq|6eY=40LQi(C$CK|1;{l@v?FL2ppY^?RZ%L=5{|t`ne%~YWU+z-~Olm
z|5X#n4+w}$)i^8&2<7|hxjxokW)ui6l`F!7U>B|aAx^=gel^eN3<wDHe-97}jOM*K
zhIAe09Uveq#(eRY2;OfXFhg!XFjaScew)_@a;15of9(KXEYv=AJz>>p!P_NXYlIyy
z-H#60sY+>TnMx7ryX-HbrRRL#{DXye&Km2@N?pYF4G>j|zSkg(Tummk#WlBK3AuEl
zi&L{G15$~&nsZ=7#S#cIJo;_;)8pgK%rY&wS(RE5mANr=Z6Eg#1@SDPrBQ4KjlMrX
zd)uosLu~g)fE+&a1hP8&Wks%r7Y|~Xbzo1g9x9%%i6ImpY64}+K#Y2ycbaFRi-I=!
zyVfyYNU33i-eKKR>I2!R@7=j(zno|1Im-{=h@FdVKKWX3a>yVclGM({6F&<a_P!|d
z`UI3jf_!P&PepHqc)x1x`j>q*uHHXo0gXi{4@a#35>oZ|)}V(w7of38kq4P$Z42od
zNpw~Z*}V40@xn%(6&ToHqEF6vzR#Qu2OD6!-38AU9cKq;Sc*}=mC>vOMx_{SWSqu}
zdV_SgRK;=3OWz*;3QvEB-ig@kd@g^u=V~;*reR{OV#~iJ{q~vxBC9pWhMZaP)KR1L
zt6rX{EVt!c{1s}v2s$%WM7EEgm*h2Q{kz;L*`*f(nnmG5A#g+UGvqNw>8pHrc<wnF
zX=voYvEW99Igov~B3>cobp=vcJay_)FFAu9XLW$MeD8kL3%FFz(ossIO;<2Kurtna
z_Cpz2Xw6Sjo^Q{|lR((spb2svK7r2TZ_3jX1HabaHY))ig^!G32yMzBrb6wKWtY42
z@9y>ymGY0*Thw&>)q^^#?0(7eZ$H1a37RKajq~4gzzD;J8L(%O7!0&E*1qWp9Fj^%
z@RF)kx?5|zuBOh2m5&Wjfs3?7ZoqIHJtzvA-xRqv1)?lGlG|Cr*5qkzF#8G^$1V`*
zo7Q0WMxZz7LTzMq%Ic=^!wf1WWrwh31<y-n%hFS~dd?K-)JlUa@3T|;{C5QuQ#r_z
zUPmPP<h<?(6>2MVHyV%{(UMt&gW_%S<n5PkpKmH(f1;`K_2rmEec70f>_9dALV<B@
z>V$Hyk<AvU$s*oc?~yKshg&YO+Ft{<gq;n!)$=z-)*zng*wz2-_gh!8`jUyrc7_K-
zvY7jW+Z9l#VSW{rLp?<S0bNCO_KbX}FLSsMdHkK%nL@0pavfm^ZSfDb)%M*?3(!3W
z^U#6m&y~}au<|L=-(IzeM?!}rap?w9r{GAVSDL)>(oN_<*n)><kuP2UXXWrxnvAJ@
zHfkVAupAbT-ATfd>gX;x{gLEpJi3g_U`w=09dd93uP6XGE~6!+ABu1?ZYc~n_Dz)#
zG&w;2m!SF>ftWopQ3DHmDTFi>{xC=o1Uk6Z%{uo|PW<_o{Z7GkYQhQM&X2fwcyGY^
z-zq17c>HK;Q>f#Y>!`jq<Pb->cYKclKYhWW@hUVZf4BXN_;;;<tT5B9%G$6}Pws9C
zJK)1-Wx+oqDfgWyXdeE>|Jgh0+0vTmuJD^CA;v$n|1^4T)zRiAs?93R4B7PGtXB#R
z++@^fgs9>PqzKmQV*qqte}X(@Sl)<|H*^9Og_X(&d)WHFQQ4TvR@fK`I{Sd%Dd!*T
zukA(@=+bwB3v0aPx4}x<HRKgF1&y^i*lvH6cOflr5OY^5@UyY|XFgoDNJYlf>Jp$9
zQm)sZY;G}mpnajI3+BNhWwVul&$eQqi5Pzh2=$!o68H58($be$h?GddDXNO9W(2-y
z4YZJ03d~|ZQ9uGSc6MpW2@{Cf6>8-b{=4#cl>J0Y8ac~&GbHva`--)~E7-BT7c;P9
z@xz^%x&;NUS~OI>z(|~z9ZHH{Btw*NM~qhCiutXu&Z9Bf%KD-beBr2YL?_xr7BV7h
zvc1z9n{-L`x*oiIu6@p(<0A)D@p+o-No%-NTI<RxR9$iE@SRPBhrA70aK?Ir?upIS
z-x8{PqmY<3`s{DygF7BaBC6onq+l7-f<DpQtm9`Y<p>vlUJSE1xggjVlf2#B7<8A>
zbngS#KVe8HXIq%;VZ{bZz3r6p;<mBbl;;nc__r5C-pzb`ITNg=(<5k1RqF8hP`*rU
zIl$`VZqo7Ad0m6XzAOqDaq%q4kg6WhH`E7KWE}O%lP`OL84wl3KSwA|uw0kAeAIJR
z-A+l(DL-W-RN^9$r2FJ)tYzx{+X!?D(|Tu&%=1Ru7A7_-AF6d3%T=AbX{H%#p;K~_
zU{z8pwX4R;dF*A`%fbvGoX71{-yqOWZG2VZMS2B{9<@q4dPLg+r(@KJVq+(fg0t+2
zxNkg?HmB`BD5pJ}-F3ev#wFO`1WBSn2FqJGfCK-X6k0PEtpjyJ3jc0;?)TJ{gjcU-
zlEkZsD+p1O6w-)=6i?`;0CR@&Jyr`d@C0QEoMdr<D>=Jf*BQOdIEzkDI?}sl$j_zj
zJp0b`l3)uW(!DC~T<OU^f}7mpdmYTXQIp>qny;=Kn~mJZ)rAk#@lP3B0Inaa1|}|N
zeCg4`XePX&_`~@Mf9c@ts~!iNyNP1&E5=-a?D=Arf2GFzI+S1wJ9byb!9*L`sS|JG
ztMiE!gRlD?G0(xi*3$5|)#u;L1w-ZZw@}qoNIb0AEFSIQeBhu4L<35ab@vHO60D={
z^IhF>Wm69F;zJMPV6!yASM_hY-9|aIfiPNeNFqfZG#+mD^V&=4#&8VB3J)|FH#2qK
zn|0hXp--OO+ch;@ZR|?Nqx>NqI6Z`#XdC7sK}{qH4=0AycDv)i<0CsB-_c&Dn}u&T
zR>NHQ_ktl76$0kOa*{R>PZopoGt=w1o&>E@T0ett0mPhP<h8)d2z0`snRb|Or5$b2
zw?M;zVl1-iR?}a#6tYAl@->rogByfLl~dzgTLW6!?<ybbO1*{8UCmGtu%zm}Ehg<c
z(5yBwXLiLa=NiJ{$q{Y?SYa8bVX&CK>(U(UL%zpQjfqX6fRBFPN3i7-BPs!CkJsOo
z|6GO%jw3{Gd9^BG5i_yGLGs3J8kaungOHubK)meOmuJHz?cVc=3X7&Ux-I+W4EyET
zxb1+Xh9!)X*;D1OewVUN5IHsmQ%<qdBQ2l$3eHQYKTI?Bnp+Id_H3T$>QGPAbBPzk
znzLRZ;z7KzyhdnWhNhlNxk*^@(DWi?$5?l(&P<~&s|MSz<^frsjROpol2n>Qfks(&
zxVRj^ARhnaxm&+`z*0Sp3rYYzludu$cESR-ARgZXbKN?;wJuNc1H6A8NpO*94cH2`
zX26Y@D)TT%$=V#f*J?tY_J->~ub-@kCOe^3wC|c!L5EMFy<si62OrseC5D8VnXG{v
zae{J%Y`EjDA#V^P?xedxAyjM)y%~RUGI6`g%2%XrA)C$4euI3E=n#eL^ebqw;eIO_
zt<38)#_4cBCV48&YbHmN``hu$tm8j#UT^OW^allk5{Ati9Z%j}s%(H#t~WpC<{1RN
zwVnMNZ|4u{%&!#CI;(%<=;+~Fuw9w_1Ama)!e6((syW@{ymwTm$X~zeb_{d90MV}W
zV0v2Uda1y|;XdGd1}d|^s|rn#KidB??M1l>(ZxSqJOwf9a0QOVmvGuJv9k0ww}-CB
zv~Pr^!j9j4^HiHQ7LuTfQg^!yV|SjsOk^7w9XO2jPf_Kybz`pCvL3CT5)`k+mhNj;
z@=BrgMG(V2cg~N%0FRP!g1uoa95%<#iG)z|0d-Ck{DQs{AIqgsVpmYVIB9h*)2K>)
zRY^xn7D1MiTL}xe;~M$KKm+a34>9kKZg_LD{s8@aa{ZXo>{rCz3zA!h6`p>n>;Pe4
zh{!fk*b14+i7ds%sTB_V&?N|@&dku1BueARxjBI+0?P1B$o%}~MDPP{*)*KgR@0SE
z$HXu@(<}~u-~}1RcgFOgLF<E|pi3q>5(96-iDX-eco)reRDs-V8s_t_aQA63Hf9oI
zw)UlE*BYx|V&%HBi=&<>%pb8vqfdn3PAVJ)#BdXVw<r*r>|s|X6cBg2vM%RuyklSL
zh*jC^{&%&m$~aF$MRPA&zp+=%&yV63&!tm`%ap41dagteA-;b{!}?jkFX=h3|1He^
zQwa@uE?wN)41%yl2*Ki)sS{#UscZUioPNZ*@3^r~TmprwZ70sI42c@U6!YPWx(zPt
z%V#jWwt9B*tjBRal=VI)s|sh^9VecbBlrOnZn$ZqNV4vT5?yplCLN`ZglzbSX6j6~
zJ|x}W2v%sYZK!15te&YChhqC(#9^b2+H%;(ZPA>gI@xPmgKcNx-305@c)z*mJ3Gn2
zgBvY<1ulI8Wr5lSLTV{1W5|b(69bJAA%2tveTb$S(xtA7o@-o7k3e0wWLEH7{~eT2
z%BIjT=4@8<ycFKZ(R<p;>QHxv$`k-j+B;f+bM91cQWSXA&5op^tax*wF1DzCrjNWg
zi27#*W?F}|Hnx~*`Hwg<5(3sK9lIKG4K(?ljYUj*?5?Onf_$)4SsN4>s=aW38=xy=
z;BpU-))~tc1}x5yVz3!)z(m|_s>t`qMPN!agq~;oSmjOAI^gK8vCD`ie0NuOYRB<}
zC!F#40-vw#S0P`P_#QezmjGlO*cTMni0ymnDk9Y_$f6VCgeTHpP-|k7&n~}YnLe&T
zYU(ohg^%9>{6KwBLXQ#2Gohwtl{%RER$14Vt3Bk0IHm+Q{4s-kb7Yl3it}|%q1Lt<
zr%-eP7;d%fZ&c|lZih-^_a~-<w~+mjKJ{=mi&Oc(fB&w|9m{(*)BHF1L1)iM)Vuw~
zA&i`rqjQ7_eJtsBO_Y3?H+^MOd(`dcvDvwpTAO(1@6|&vhZr|otn{~a{K@$rihrr@
z2v*GOWG62XD%yn($l(4anOUP~{%mRclYq^uO*8vurMg#t5(Rk_VRmLIMug;sp2KJk
zu;e86L0ZlJ*<x9;zS*zt7zS)ZSQ%X6ZVvhd!(=OVE+7~(2YVBM(6PjrDe_A=ei~jj
zYw0aLNs|VVJV1ofTl>U~j$HT{G{T|dfcT5|2Bb@m#iinE*5x$EnpBJ9>!l0ADYUG#
zSKcR`bHVnBp5fnm*fT<}_iv|_qh<7(ygzVd?^dZ*o-EzOm(MkA$QnnG4tCJLput6n
z10l>7%iu0FI$Po3Re<FbxD<!3(g|YoO=Bqa8bYc+y34S@zbgRS(5IvmzX!ZscBl?=
zb>ZE62a~R%U(-pRs}a}6>i^tb&nEA|7YbHNJ9>RqxktTZ`rKd^MA4Fy{UxdC;2f`5
zF1Pi+Kdgtw_V(L!miHYN8VvS7F>*GZbk-x)VB-7&sJe-Yow~-OYg-q^->HU9Wv<XR
zy#-ERhj>qt%9#f?t0e{3Zs#UJb#iGQ9sgsuy|CL9OrJ%htq?MvSx5hpHiVpeSQduh
zDXJOm(ISqo<5K)peAD8tfFY+d!1>JH_Tf9|`nGC&C4~8{?@UF>2DnH|mSHqP%>stu
zkpL*VmHx|87$+?Xp+hp>l3Xx;6?+xE0W0T)7O7s46n;`}<Y6GB$K}GDK<hG(mAtV<
z{@qEletrOE)x2$l4jN8Yb%;&&p?1PDEsBVY-hT6&h5Ch*vq51mdsK_yPo<7KTaQKT
z->G+7>grssHNH~@&@_zDH7dq8%ue*mlImlp$!?=TkZ2a(2(f4-&ZQGhrB`;6qT`|5
z(k_O-1Pz~=#q15${FQrcC0(%<?AUwBJ1kf3;bArI;f%(RZqyulLb|iF-oF6P4lq)S
zBble`4h_bv386?KLESyo@eS86$rYXOel>f7*Q{5Cc36)BRyewxS;tO5AgJOC#vz|1
zs*ABM>rObSOr(lHHYpzN38s5R3u2?J*@`EVgN5@_UyMiOG&fYa3^RhaWvZz9;Na7f
zA$p`Ve07Amt9b<$HGP1OPvof~9~#uDX4fdGfQ~U6toIXrRRqn~tgq{FU~}WwL?{Qd
z=TPk%T1got^XBW|$_LW?5_ArS&j+8&VjektG6B%dqI+WNnGH4q&vv9g1BvXL49a}R
z9}j_jw(Ck8XajUoa}NF5se03;^P4nko~;3>pszWsA7XQC+s%8Wm=Pnp&rWy^ETK<j
zS6l+Sli9)^q|$2o(DQ|f=A?Ey*v=eCM!v|2ddUyfkNENfXzo@@78b^mIqr|M!I(le
zR0?c{U9g%rf$fgsEX@^YXo{U)lQw^I<uA~SQMMK2(_SfnLY?+U7`c@U?O<;1x3AZp
z<(3K)Fo<>im~_q}GjHZ~pLP9+#IQOrLoG%$IUtQ;>iqR)Fc~_C$q1@1yJY*HNp-MJ
zKj-9E7eAtvgpGh-6blDT2UFL5?V;5w{_JCuyp3T%ZAqs31McPCs5xy#xX85NR<|_=
z$*fS_aS6~LDDwVAV%zRo&%Ow}Ss_V#id#gY=gpE0P%@2hn?#7;;UCD8I&8$%#Af&$
z?r+1FYTTft_(H}dD~uziUG%QGgIb^KgUI6&qMC@fJIl2gYZ2?mxD5|czQA}%GJ_wc
ziWJU$?|<eCA@7`$Q?K32A(y_}b)|wx?dJu|wp208<_mL*ooBF~CvzFp=iM3&rFg^N
zvEJ3OZ*|3DBo<6170ziMi}=^yDv1;KCgZn6yXdKDmAMnAN)*}KDL)8+Gz-Snpv5%4
zA;fP$gL{C;swqed_!|$xW(-P!r1tNpLbE1*+!!s?DF(7w1gKBZBZxL2%zQ!Vyw0yp
zk6hoY|8f=Z1-K5BE6XgY$564xtPPr-l05}>Ft_+T^45NTKa)aH-%)TL9((Qy;0P&^
zT@zv5ANcd|ut>BGo@+@4gzks&yOZ=rx3exu<-d?C8s~|{*9imY>s;@nl26xWxwAGi
z${O1ZL~#^sU(HI(UjMYE^|j5nNh~Mw5{|WB-h}u(R4)%9N$Vp6>w8Zp|5iir0ZH3!
zY@U3=h+i`IzOjM(RRN`N;7eM5Y!@C=7tF3ShE<qJ4(w&M3YkT<5AOhU!j#MH`_wQT
z-n`S){!Q1jaN>~)=gntlCbZT3+oZ8uFg{YdBtD&wu+v!|rvF1!0Ub19AFltG|974O
zPm$*=Loxh~zv+(d6jH;3jn2Ussn5T3h7{bX=7_m#f^K+Nix(jjg(zO|W%k+p+C^a(
zJ}K<v^B&VJ(B#_6HMMYIIFW56OcbY~Q$J%OlpOKHfDZsQWK#7qY)UiRNZR2n{slL)
zO?l7gkO9&M8eGt9PQuU`gH=|6AkZu7t1%Us5=Gl6wn3JAm25)U!AMtpe`C#D1QPrv
ziVXf)B}}@WeOk#BB%FBd<wL3ti~^X?<vJ2{2lSd%Lwu=HLffjpVI8xpS)B$79fVe2
zlPcCR6;DsaY-Iir5`J5-431nyDb!V7pDy7ij0@1jP~5(OIbG{!L79HZb0nCe)AAB{
zW(fJ7H<P0nshnb*AuiB_4`#MXbO8JmFQsD?D&jq4K~#0R8Suhd?Y4%bv(wRoi*%DF
zHmLfSjFl^-5<UJ(R4+2uLKM~_=Xrt<$*d6KWzCs3vM8TzQ*${Y2%}0fWWC8QbUkH!
zXdxuhs#b+d*O(jnB87fdGJsviKrb|PnGY6}twS$A)IB40InM4>J(a1)!aD|htKUv|
z$fW7>LwmFD)Yzx+Q;3}xj#q{pbYK@ETVP*Ew?c^;O!iIwx7WFDixT`l?$^(LaSjfr
zJ?dLsLpQ+P8fkJJL|#PY$*5{N#ps1XmREvqyT&r75HA=_pJz}zUt03xmh-ZCg@&qM
zkBirJ-`k>+iMr$Pup$oqd1#DU4Ag15OJE%wbK&k>d5@if>=48}f17PpTW!r^DuP&U
zNW<m4Pa#^kri6eC3f2lCbTWF)R~80*tRIOx1BFiYTU7N%$;7REgUetP|6|RHvcw23
zoAarJv|pvRs&TAVdwdR5h_+JoLo!(LJ1=VeGe6v6_R3dMX-LQj%T^(oh?J{dr$&IH
zF}im`ft+oYv^00EuZNeTq=r4212$*>j#&tolAA&%E&v#}+BUU|fbtiU4NJNa*QZz;
z)lq}Y*M98oKV>!Olinlu@3P2x+vU#=3kViC9GwOlSz6<<I<<uB?R{2Vyk)gJYCaNb
z1VJ(MLx4?|MN0=X?bj5PJk*p#=^x4LITIQQHc1mRu>qGkq$1!Ahn(s-!bu7s<;6yg
zRVk>4#e1K@KeWG7n872F#Rs5oVjMF<c}>X+Z2zo|5OUCnc$D_{lECT*S*HlSM+tp8
zYv?OVTIrCtP@>f!_sS~@jidAUjj9j2XWDJtl?eU2Gp>1W#u-t|&6M!SgT;_>sflF?
zb++z)H^y1<%b<fHzSW=z1FP~beCW%5!Ub<zuLY0ow4{EO=O+%jXI@?1jUijmy3#n{
zNep)E(6__5K5RR-5;XFdFtVkvE}7;IzP;ks(FNJV1$EQy!|T*iooEJk<z7~{*C4+E
zb(p`~sgX$u%y`d3Li3LK=)+2S4Ed{c*hUFFm2xdn2pYiimGbemc3geWk$x#j{h4nh
z57}kS{N?q>a2+W;wRhR@i3wM(6jM>_9}t4c<%DdkC(-khsJQ_Y)dJ9c&3bq9V_4go
zvC$2+>5Uv9wJVNiVy!W5(RgCIFa$(NH4^#7wwELi#r%nRoggyymAj+E2SXu0J{#um
zynAo}1|-<Kb~B&6hE51K1m4&c>&|S7rQcx5<=xATm5?g~xPw?)|L-@b&|1TB4eA8V
z3!Vy&zS_onXsFdV-^#{NFQeg%Mdem)S&l`?p;N%!K;0=}J!#3ZZiqa}UIH@HxC-qM
zX<YcwsbEuXf#h)E4*6<Bn{yNd9JuL37c=Osa5oT)5HQfvxhme<&6oq+Z;UCOzB>rv
zhA_i{Sd7mT`?sbhiB-n)VvFLJltTSSolQOYa~5B;;*O+KfIg~{YLqgc!vkB~6%F-<
zhsmbIi?jFnM+u}`B#x9`qK+%LRuXC#!^R981iz&Lcleo)yhCa@GSA1KmWC{@cZ2%N
zpd9K4^YIcnek7fqF4b8Fm8;Sl)i@q%d7wHiRl|AUR>Dr8^E-<GXM~ZlHHtLSuFtrn
zpd+GyttY^`3cOy1H9chNxp@Dg(ck{>_%-br^cjgKnI{}oGPwiUVcl%t+>#RH+4xvd
zl<#JoRtjO|<P0dKh!jG?E}zDnC}cFZ<NH3{F4XeU7LJGs;>7C*DLvq_qyxf)U;H+{
zeug2V7tyGIZeRk;vQ913miqMj#tZ?qzpBO88@+l9s!*xBgX59wBR1R7oM;B69E7@#
zSXCKkzK7}|_p#Z@Vfy5VY!+S_=<P>vC+^_Gt`|dEfUODKPojLDNUG9bM!KDI^q+4w
zK$71B5bk%--_hiv12+su#^~pQsarGm6Oy&~8FU^kjd3GB`-ICIG`;FGc>hQ@!q=jT
z)A}Lyg;&%l1)=HE@En>>>y~G{s*CTWqK5Twcfc~^5)ff~_u|wc#rpZaU!IQaNE@W8
z2-se4SdJ7UCk~YzOPhP?&(mn~w~r*1m#NxQl>~=5n!F)Y^f->O<EP#}%U{K+Z9!k^
z!qa<u#AUJvxS$xLCICJzqB_24UH%GCP{1XFxGY5^GzIszkC7ht7RKP&BQgrhU_-m7
z@+_a%qsfhif2y?U2(-;&M*z(`{EWTHkFAe)X^@$y>jC&ndk=Xb`nY2%@qy=~-rciN
z(kJ5(e!o2RaaenZNf^E2lI^1iC>98eH#J*{G^(5}ixv<+gOwPKM_ASGg6;g41v2ro
z>e80>GD0SaQf^J0wq4K|bL^uxz8f;vWd(BSsDOVN?UKRlaz0K`QJY<p*qxp{&%Q_2
zCMjzTLU_aKp;>xHyuQ1^aD>tGN((zDPMu=or15fXxMYgrka|K`XN>rebN1>mUCoWb
zwTHIz(_Krol>X(%Cms8psp=fpsO&&};n!H|?C@c~QyNi_QzN@dl=@wgEJgG^<WIdQ
z;|O<NdS2YMh-p5nHRe0v&Lmma({zdT`xMNMV0?-;rDw@^J;02PFKeFDQ?mLTxE>-U
zdwh+MH7RX>udNKzO{v!Q6QpkGNyRa(@%z~C9q;dFb_s!s#|+SQ4SuWncE{mP>QqI4
zG)Vn>&Ua7{jhym}!9*RMtp&xeULRN6xsnH<?!Of3dWVxjvrZPA^;aIEu<7JAIplTN
zj+t-EitWySM7LRiTB0RGs+;)bS|Harrk<j~U@uNk3fXHdkIA=T3Wi7Gl`4~kp7=*w
z1r@g{#GM{?$c!~@RQFt)_A3COq#WOIEdzxrzAjK2M*-^m+)ID_6?k^ZWc`gPl3PUt
z*-S;t(quF<_nj|sbEFq)##xO2p54ZZ=CZ7h8KW1-h=7&9`)DU2a4MorA<35|@M<Rn
z%_-I>YSAeZHOR8T^RW=|Zx(LG_Pi)6#}?4Gi&!yQpxjc^??^9$C{qVWLUELDYzc3v
zG$;Y};zgG@9D=_=#$w@MUla~hi=&tj-tvYwWw~<B>lpBJB*@}8x)o`Fx<+kUbHZX8
ztbW?md4W*}m0(Dt5~>}xYYvdWsDyhwAP|EWBPSYsZ09NBKkd5-fGaE%4GS9IdG)#-
zK=o(aBbR);<@qoWIrpI<g@%zYYwmp_y#z7xo_nxrT)MSq??%EG9nzgNgOK?tac?)F
zWZda~-;Js?+6`O`99D#tCvIWCg_19ivQBZdr4IB|_U*H(Jk3r+Eb#>>?Z@gk;14?c
zHF6%KS6>=t>?xm5m&>SN0AM7%etpLmjo37pN5j4a+Mr<9M*D%|#jb2(aKyUK*e(ul
z+zZ-4fr8%*l&$Qo<k4DajAeTx2JfjncS7_ZK7r8XErJ#tOVOe@Ao$cIv^)#@kx}kj
z3Q-jJ@Vt^ZafHDB$!C_3^r$Fk4dMMbJKvfCKSS#B*s-Hu(!|im&QB`@bIsLWh$kYk
zHI3v~2q<$8t16VzGO=CvF!$|Z3G1?aQr{AjJ74-pCXZrcjp+8kn}bBcRHvXYBr@gX
z`_{hrKHwSvWRNm+J&n5^v$n&~-Z56AD*d%0KI;5B^@aVEBBH1nUU^ZTQI?YP>&Xji
z!S0Ge>z@;UHyznKM{SYgUn)YmBL=sK`}kdYmGm1c+!P$$kA{tgdGK`dOX<6Qk5aY(
znmsNjMLR3&nw+8)-&~Hfu8N-mCK_P|vIW^3=(%&|06yn&8>z#OTgHy8E51BRd<du}
zl_~_C-hkO%EI9VuIt*eVbtqj5$W88{SiDI7-;mXyc|`~^iyDqLf+}G%<^aP5D++<v
zNJjLIja2QQEr?CLFLzMC7Mosc$nWnj?E*St<t-nRUkNMohT7^wKzE}MG<I5g=i-DH
z<>A@EeR7#_GrmTiVcEg1W-&n^Onf>^r>47n+QX$RWMKgU77xFnZk5c^BXCX#zN|mh
z?C-2k)Pv7hz^r7pR}`Ols8Md+-XcE#<zO@|hz}r9;==@H<*XEX_U02_?N{n-Z-W6V
z4->$nK2!*c*o5KR>qmtDjpt>`;+^f6Dy0vG$ZSWLaQKZ}qVr}UXr*2&Z@#La+*-#a
z4VtZ{lVhdUse(<pPq{~y#ZO>KA3vl-v*3m%tsh<IFg8b73{65j4j8yqEp3P@e)~`|
z85v&}Sb5$)fCy_cLRCOCP5(pMt4V#(D!Ftuz(&uX(4lx+%RMN}*x_Ffc`$HKu$*L^
z+|8|*9hRbTIs*PJ7wPa_wpC*Xf;7z@@@#?10XEg~vW}X5C{sJgoo_3Awb>#5M}j#*
zT!vdXTcqg^Z5)G@mhg2YlnO}MRQ<DaC>n0y_2~p)=eA4m{_`_vWG(L`@(|cjI5Y*J
zavy`wO%EcjVB*)F{`~9|487q)c<^t~D_#@}^=(8zkb|B#HDw~$c1HDawre`~UMQ#V
z*|GHWMat?3iwq%>2PpZ}d+VxURyS`4{M7_<vxP6+`>FQZM@M<4a%p~MslUY|-8EAO
z-_X(qBeH?G8Tj(0#-1<4a`IuDe6J5`I)Q#dcf(m1cL?DH1kYqYM;+JY_f*0j=&FCz
zVtm|tr@jG+_-21JRH;lL?;TiXPY;g5w1-A1zQqX`3~P!uxG%}j2$)%bnkx!q!<sRo
zb2-ujOH@`}*a+i>L}nLT|3=t7%x)7!`WA-x@<Y?Pu4n1Q@$yl}J~ylH*pUSa4sVUw
zY~yvOcOF!`8yXREy&#J}_SRPT;cG0=ES_#Xgk)*exMj3qzk{NfolqUW6qb$EtZ!#R
zcAg#TX6lU*wt-*>{ngG!aBWoAGa?8q?x1KJDSTXtQ4ph8^>8*>7T-1di8_k6#(Zwi
z?5t4<J0=iVPDq;=<w^INhk#gT3;_O#f`S-ux$dS@p*E_xEhfdEgPN4u$Ws$A9<J%v
zAMmcu%(Y=564-+gN-XL1z<T3<7@<q#*$j#aI^M<Jr^82+@CD$>9VGf|CQ-ujgdzZV
zVit)3CNUA2hfPp3FW8_>{j+-#kmhF&qCFz@{0+=nSN>-B;O^migDnm*%UC+Tx+Nv6
z%XY=zF})`RQ_X6j7i)*_*u&di%J7CdG{A_#a=Z#g!VDKHO9(n`XgdJ0X*Yxr{`DRM
zgkai&ujHyM8oPI(D2*<8BF8bY`8v;!;H&#a(`lG@#0D=dDci-%p;u>jc|GysjrCDI
zmDKvBxfVAr<c=jY%;LDpd~$QbB?@MAy8Epeulu5<{~Y=ed>RJS%ioWfZvKy|!A_X-
zwBP6SEf+NQaEQ)wwPs?C+-<eKyBvY9&Qz`k+3nJD;u&cE*?vSsf|Mb2)AwBn`VA{m
ziH&TB&t)~RL`lB%kB>Ju+wCe*N6((<(^d-$D%{CtC?KQ~NdnG5T$4adY)0vL-r(Q%
z#%Dnc<G6P3#~hYCvkWSWST4Bzd|NvkolGR9zXqc9CEEkuQG`jOG)BF5R_P?C-FH=z
zjurEev(Q7Y4v!sZqv<po(_TGG7p`;zS|X1Y<fxjELt)4RS_IEJ<#@MiF4m_NIt_WH
zRUF6JpdK{rp3~NCddo(1f)!T#9dLPFSDJqOz=RO);F{8}?`uf(`W16?+n%6z%@~#L
zXf~J8yRW_RUw)tkWX!D<8oC7<R2|B1>I)s+tER=~sf1Lu5hAv?G8Plk0!zkw*UTCR
z=osrgogE0|5pjzimY?s%ZXr>{^kP*5xT`t@97rU{QySmlTNXGdghOlA9iHT0c{d3&
zL%TrST+oo>z(LcNLP)(#zQv43RUVqWWH4E<<z)t@(k-n>EZ2FxcP$Xft0*S`Y!|nM
zl7+yTrE7<AkQHa{GP6RWg&|_kBv|dX5-$4cm-js1P5V5p0?jh1e}WVgM;pgRNt=`7
z7D}fhlf8riUC+1|3yG)Vicv}Qf9k1q@sI^pqj&54xn%OH*A?%5@GX6M7P7`Dg&7xz
zmoWlTu7THjLRGy=NX%`)9QTicF$MJ~d;U>B%;7KOVhyh|pTpCaRuN2tInK@N%RQ+;
zh`mk@egWOj97k<t$6FgQ_062oPJOWQqLb5rRc#f^WjP2)GBCJ*v84?MK?pySw06}q
z2nKZ%jG=ju>NxS9ucOgFjxv{>hC0y;TaW2Cu4X^CcL$QA7l13Qs-}NpXynmW6I;Kl
zRe57&*&|BJD<A~;PgBpUQ-PI4E0RnX;plnV)6;D9W~6imxWnheomucsHEpl&P?gNe
z6a;JuFhKefHdh7j>wZobAgtMS>zQ5R9;}J##N<Qvg%zUow9;EpA(QtyF<7GdS+18l
zADGpEf^Wt$wMhJXl3LQ`B^Zq2B%<?WaXycbKO)|M;7lVnK?Llu69)}Bs8y0EW$EmO
zniw~))n^H#=nSH7lH6`~xiJB@45It%hBkgBUDwC)h3yg0r*HYjNg|=fd8vz(xaM`=
z^zU(OXerIh%vBH_TIl}O(JHeqIcI9A=GCtM_HK~b7Q_(e@8IswVU{+P*^SM7+e6id
zpO7Z_KJmSGz5nnXkU3OO>BkSu(7m3-GBVwWLeCRGD~G1BhU6_wuG!?=9IZ3CY6|7i
zfh6N1<)kJyJDa7NoW`}-@sUmz-rtn4>yPrW#0GVTce`#M$@dp6T?Y8wZ`qik^|Wi^
z7V=VEALO^|yWWAzn9Fp=ucd6g+xoC<?4P94U(YI`3wbQUdqm{T8^cz*XAA22QUX{I
zX<FAM{MMx^vX>JzT|~(T4CT~(1$AU^IRmTF!5uMni*=iqX`<)!z5_SZrt>P7yr(4z
zC}_DI)kW@S4^i3?hC3Tysok3%qdKy^lx!8k8Yjx|8SeXI1rq-9M#(SEq|1HR`;8jy
zDLq42Q@he&7|{7%04GoSwKw>qUdY&j@8-I>_mmdRCCh-6#dc+|+E=u{8{&*HPc-NW
z_6jU}{4uc<+Kc;G2bR-GkMx=8fr-RZ>TS(Wqd5DBtjjGT1=1^PnmRBdM=~b{=d)fF
zHtEbJ{hc*_pJ)5KQJ(rw9N*MW1Ep&;V(|<rV`{HaaaIZTCg$Tn+h1gnxk?(C+}`}=
zVc~F!)U0CWV0DM~f7uLvcw~{3e0$yXBPx7|kv+&x*`tBU3WtZE21%2)kkKMac9bVL
zmCTDY#5UZ#(7;bsu8WJ!N4c%@K2YgwiR!IB(#s_2bKUpcDb&@7$F3yDD@j-L1iNPP
zY<8bh<R^YxUrN*8L>jobUcAJ2Ley_(H3lbp=9*=38WV=GEm1#>N}^Y-in<8}h4<eu
zL|?M&bc^Z1dZ|Lp`>D&&Rw?MlomWKU+$>*MZBUYmgIM3s9%S>nJoN4Ozu~4tXmYQk
z8o>5Rks7`FZR&<NbGjBke5L6l-vP>Aw+%%~W9(-QsyCRN50Z??!h(!!)H!_t+Ya)Z
zi|ZuQR&eX%9lD)T7HaH`c^z+-+jQt8Tph<@yrJ8P=HA;UQsI6&X%E$TqvVfvSw((5
za+JVL?F^YY&n;Aymxqppzd;B1uvF%S(a4@`fQ4@KX7aWe{Bdk|IOS`Wlce)Hgv5}I
zM_mdu?CKbU7QLS>G>?U9cc7NeT}Q3ydv@!HPVVVHE->)S92h&_;dWZjC^5Fn$LGuE
z1v73CI}0JNXFE%H``y9`IUpkQj|!UqeaB{l&?h*>#2~h~38gcwL+xx|R797fAu=@E
z1njfEJbQ{pfx_sxF*VLL-2I}cSwK=?3})IVWHb*C2Ac>)0*6NGR&<{h20{nx6xhj$
zh0-L~KL3OwYSj)Z8U>cUmp>K}{#Vva{zpEkspcIh3dK_xNkMJz*q%o+4GPBqd7BmH
zjEYs(C?z>8r_!N4NIWO8tfE!X9@kHSLtHc%x)0pSvM|+`c*@2Qn^mavun$y|$9(TQ
z=zHg22j;vhuLAK>#msk=<Z6l)Dh0T&gj(&hsD^nA&CC{EPtx$n02er3fuUbn2$WfE
zq<S~X*3s}8agB3l4(ZPur(8S8FToYW#Z#IkfOPs(81=5Y+0KI<{kKYR@2R2?*S6M+
zxGWT`wFAEVi3$$lkXLQYh8KEP&jL(a&t4ENBvy3SwSSv}G<6^<P3a%sX-hlC()m{)
zP!?}C!8qDkKi`A#zzVbg=d4Ijd0$&2+@hv9C}<UPwjy!cc9$g$DY}bUh$>4}6)p8X
zai49lZ#)wr3(J#9aVly79$WdujMjW-ZyOQRYK~0P=soe{gz!t!5E=fo-fH#dn@2QO
zto7M?=X-_9c^U^*!zkauz`rbo^ue8>8u$$MYeQC5?eAag8j6^FRs**Nmt@bZj`Q~s
z+Zh&!K%o5)?JYJI^-rbl1e7ee1!1#VkmbN_(W$dTLU?_OrMZ1*X@BJLPz~+!C*A&F
z&Kq6tA<OD5Xzl>>dq@<lE~eRqhyrkE+=XVh0upjLbCGi5nec&ChonGs|Mo$wKjD$%
zouofvv%+oD!Jy(91|~E~ikly4)*c2AI45fTyodha_TptkMPPoBo~D<0Y_~~X9h!QU
z1&dV`>hcf}pkFoHEHKi%E&^N>;%v|KDU;^E{;rWm1M`C30Cv*bn+IzYhk*<CGp#I1
zNWgPe1Xn(Q*P&c`&v}JRv($gpnXGM&K6O<)xzNP+IU^Kr$j~|~fLfQD^x2{kSNP-M
zfSEp-!hfy6Ra6!HQlr^(5k$>~xEZ)+-#+2c6{7BFLDaHe$7)4z40DqWr~P2*TFgik
z<hQ~XVliAoWHR&&Fyh<0sQiYP>O1}v^lQIA4=4a*P(G)L*0Ypa)+AV?>m+qjJB_x?
z9trRx+WF)Rpux!DiL!vu+54=1_FoWYg)xV1@k$Nya_4U~=k~;}SvyB+j68ixR_UkW
z;oDqwpN(_^j1_6kUv8j-7H!c5F*se5)KT2CP@<`CsJJOXS?S`VbFQ2`3mYjsh%&&*
z6|2NX!6h>-8+gFpJB3f(lv$4UQ<5BQ;Y%SAVhXQi5lzNRd#4Q*q$$zn2!J>gEQwA)
zO^7B)R+l~RHlBY^k@%5YnzEMC8@R}HqB~KE<{(w!)8?go?LpMeaA9~=6?*SLv-?_;
znW;R@DG;Jbc0Nl)ez@8$uxF3x5gP7se-1^1d-l6~p*$wvkCD<TGaoon2Q)j}tm3|0
z$-OqoeCa?@c2qe$mNp4VDJ3j1`K`vl7N{Il3cW|Br;+=grvsoK<472zGP4(8vBV;X
zy({X!&To>|P)#|dJ-$EY*$<5#o1HC(ln_I5A&6-D_PWV5S7?ywh7AHJxM)WQsO@?7
zN8>$ZQT7G9GnRR1<2fTOIhK{5B?fbF5N69N7vqGOL;sDWndxE(Rs+RiA_73r*SMeL
zWAF<bBuf7D=-<^fka#b_No)H!ir_84+Swqx>+O4BhauoSw`Z>)wt@zJ7szqjC##pF
zSZQ-~#>ieVEtnH=D#M!->eX!=oY(+8@l$`oFD}+(3}^uF2sC4ZW*Ej^kP|@cS3X7+
z{I#zTBro?hZ*U<Qxpb+Fpkac8LEUHyCYW@K`lTn<4is>x7ZUv-+=jLOwtWK_gzEZ0
zufmli(ZYD^Z%Qc??as|&d+|nVWj$Ec#AKEnN9g{Gg~r-1567(}Zs+Hgu`NTPrlWLb
z_n53qj27OwLEy)4a8sV)`cw&r)56eogTNlZA>HC7`*sMbp}?8jV(Y9w9lBQRBZa<v
z6#eUjP%oN%!_L4`E-jq9S}^-@;=+|oi6tIDVe1<df*v0r^k=jbM$_}J(zNc8ZRW4g
zpa(J5KpCOH_)2mD$G+hYpQl|RafcIlXq&j=d^49LEb32vW2`Lb{s>vJ;Oyd~h=Rdu
zSw%YC?}`VI^rEZyE&ajPuG*ZAI+E_<X(bD+3~fr3hC194M9;o|k6{9<Cpb+4nPS5r
zWYd#!%BN4)Va#WL2NcUUz}g+NJPChQ>p~2&j@Y?L+BU&96;Bsv`#nZ$Xc!pGzVY0K
zG;Jw>>#`O%dJRSTxMU3s-CwUZ<k3m5Blzj%Uq}d>IjH$uai-N8nCYOZqR%PF6z<^t
z3NIaV5x}g^%dlo1VAq~^{OfD>9kRynj!yKHbasP~#m1171O2Q&&xTFU>_#!@>;BF5
zgkFJ?ETI;JH#41_^=DQdLc|p5q|k>%c0WQ4f<8(@9C(e@=WY&Lw`u~Ocj{(B@)4cd
z9q&lyH$0F+*fmdrvIzMdlU?GJJ@Jk9X`!(i*{9K5Y@+)*T9q2;y1>a;K3b>h4brbG
zjHLn%*LF&f)8W5x&bDRscmP@OyTAJ-4VQPtVj~Phm>E;HDg&)wxZf1?^LwA!Dtf90
z-4177mLKitv{r|Y8jINs-SP`N^^qUW{B^B+C7?;8u0r3ZqXU0Q!j8TJNKcimJ<1=1
z^c_>(<~nw>NpiylnB=riC`Eg5v@U*GjjLeWZ@kA;npAHSuDDmh3Vo<SlF%I9x(Dc}
z5$AC#KqOD2K*MJ~FYvowB!4)5vK)y%Gg7#VZJa~nUcl1mvVN=z7K+I-4)tt{h!v$`
z_sm4pMWTkjD%41ox*rS*71!#^4__w+w+M$@hcz7W{u<B-jVhppL>z5?<Gwv?x_kSJ
zEbgyM(jExXqZaA$)Lq2p)tE7juo_1T;fcXRp;E(zTxaiMp(7glK`}&A$GB<2ZyCg;
ze?SpCY2V1HfxOk^YbCwc*v{HVvcxH+qJM#JkEHF{*90+SQZb9`))x(`R!gk$-YHCH
zlWgfH&pFNbArwEL-2$BkQ(7#vD*3LdJ+|kUHmdd}-WL)=&_X;7%q;q`CqIB5=M9}*
zezq$??n0M|OqUrwLRQnIpowp(1TCS(k>5tVn8;UWNTa|InI~q*8m3ezdT_0lqa@3m
zx4vnTZFnK+|GYX*S1Yf-k}|W{=<<$Q?jMPT|K`pKLQg;LD6*%D55(9<@F}lGi6PpJ
ziM%C3OaI4KFJ-O6jgD^~lh{9I<}a|m;nZe6Slilsos3kfjTDkJKDs2STAx0@eK|0e
zYm!bEW=rDR<Y29H-65#of-$VnmewtMfPz_yOn`~;SJZw!fD1+HS!3YJ7N+7u@bfp#
zN0N)T|KAb9&dYlq$dyRAT+kb&P?d{iTpzxDi=y<-ol3O}Z?xbLYQ+2(Og+Tke(<YZ
z@X)8YdVJ4d767aH`LC+K9y)XNY%fZ^OPvVKzRk#vO+W1^OwjYD{zqy^zW9=|Wmara
zu52sK5yEP@R^#{QnelU!INb_5sU1z;&zUP({Hb4)5$*n~KVQpdXL8Xy4$))0ooN;9
zq^}krY$P!R67d#a@K<crM$3D~WwMtxu<S{uz<1}{v4AVa+XLU$yh|xVwD~)W`n=xU
z-CpqQ;S|vzmq_NNrHL7T`GAayRM@kbH$M*Cc<uA(ID8I3F*D<H*RuMTb=Lv0*@69v
z7?W_jn9U#)(!wJ8B}@9}AB~u1VG~Ym4HR{*+mIH@^)M)bXV753ec3}8W`JOuA`5r7
zjio;}idwEL8?EJe0(;*n@k#q_>K1M&p6oO|oi6zy<9Fi+&IeBQ-)a9P%p<%j%X&dO
zlYn$>ZNfgV44}930OmCX182+EBn^87aq>`KWq8-sh6AC4;;RqMa_5e2DUz_^h(u?i
zGwz9ba8^rvP+CW~MeRD+Lks@u^*a9DaQaKA$lu3KeKQr_h0rmYYri_PSxJR>iGTi{
zl5l7_*KU4u$d|_ZfS~-_>*$H7oU{nM%)+QMM39l6I&>tg7sZ<40J+uEuX>a=s}9D?
zw)0V&S2Rt<C-IoPSw|*G)`q=4=B5YP7xxH#XS*YsxsFO>M(WsdLZjbBXO#Q>^28OT
zwu%^mDFBprbpsjly#X$r0Z?Vp1Dy#_d|crV`G=MxO`>rbwn_>TnFwah)@baqJTXe$
zD_}?HJ`{0Q=x;tgRP2;cJ@IPAtbRH5x|~o3iaV8?52l(AKZOw{CT%m+Z(3BhWfd8y
zj3Whv`ELHn^5u(3$Ep2*sr;**B2NC3L{ypu4zELh90>k)mZ>mPvE1w7#2l_l8C#yq
z0!ifa)<B>x-%b!VSM^e;-<oDL?(9qRm^|W!pb3_$mr-OTiGd|jhj)+j_H_3%7!;52
zQ#a|@^HyG*0PkO`4}Y1Z-@t|DaxW3gegPAfFhekK_vf;=gW({7P~t3o6u!~d7`OfB
zBV@PhRLFrx`X1BwY*47QBQRl~ZK~jkBs3kSv=Ob$SF=Ro-tG%+nKE(ul+`wxJve1^
zf_Rf?$#DKLPHo1p`GGj>`Ua}y8*Y1Xn)_QM67<<RLT7<9;Rm{9+<p02-zB3-7Sjie
z{GvTAw)@8{%6RqyQ2JF)tU+40$a%CdCOMkb%ZFLuvivs?5Q3;+28op*|5r1W9~1}>
z4X9d3A8=~9iOp-Z`tML%X@o-JIPG2uJEo4jHvQJaI7K-JO2mMPGKQN3X$olhtAee7
z$o!|}TJEF!>BiV0c<h1X_f>tn=J)op7zgV#j*8~%>FZ7Ar@;UZ_wB?cRDIA^?R4K~
zdyn&p;*|sIh-9((3Eft_cv2G|+BVG?Y??S4wPqXKp#W1Y7;CRzGxoxJyd*w<@e*-2
zqUb|U;qG`hUf4HDV8hI>$<C5NN7dXv(UIieza!m*<&yLyGTn1lCS!ygKc>_!;2@ea
z!J`&TMVrDV>tsNbZF)uxp&DdnIZX$6Ngj-;LMn!s?ym}4+)c&1Dt`Laka^#fO%O{t
z90;}gB$<3_<sTW5Uv-XJ3UcVXIdWzs*&%43ZlUAR+?`MhBELPiYpnfKqA9r5NUx%z
z2GiYorp`Ex6)U-zxMiVtR+WjP0B5B|MWafCF96s>;ox)Dp#ZFq#<&&N+}1`+BGc@Z
zD#UuZy2Xo>edI%;^+F5scMl1Zf-<%pk2HUQ-VxQ0x?@e(EQ!5AIZAzzJ&Rd2u<UAe
zPVx?)lT&$ADR<>h-7Y1-`!!zylo3wm5u+Avq6xst-nKZ9?`pFKhPrRqSrg=+=#~`8
zZMAxxcCe)UrC&H2L5{Va=F2Zt=7pLa!T!^#2{80hp5DYN%+|%6KC#DJro;EjoSqr^
z*K<SganluE^t%gHAPoPhe?A|h(9x86@<{WQjX6NK`N8mY6D3>QiK@&-bN0ezF1pH^
zL-YkV?o3l|T?!gZjifef@9jzFSuqS#c45V4{vurg)K!HHo7ASS!yjGF$C6{E(;#)0
z2TBSs?SmU-MI7{n<KaeGIrp{EYu(dSd5q!Aeai!B6Hd%3`2zzs!d^H-#-1=?n#sGi
zogy`n7Ho26QrSueZgL-c=g*Xv>?g!{TBmNQHYOD&=f}ktww&>8w2D<YO?pF$LVJqf
zTxdPrij3K;+?Z9M(V_d=;!3II%YMn3Rs<Xi5{J%Z^u*OIl;=1Fj}J{jqSNG8*QP3!
zqn>{wpr)c8QZ9t8_F0)^F(ULqd%Mr*;vKB0@>rKPFYUeFzq@4+IrP?Uzw|EVL1DG0
zQ6cyYvC1U`1T7#zAze^{cA7APq<a`%M`>Olhm}@G5>@i&BhFIzxSlhp?jrJ0{a!E6
zbW2S0)G8h39#>B{kJ+RDPhsBx9$B|6+Ocg+Y}>Xov7L!Cv2EM7ImyJfZA~Ve*yii`
z&$;)Wcfa?2ue#T+TB~Z+^6rhURUc|BRvbui8=C92rH$EU*(BIHxzISAc_6a`mdIy)
zXwhvk`+2L^ao_xUqC?((l*qv+EDdI;8Zun|L78tQK9XvAvN;`;U;_QNM0q7Mv=mR!
zwukjl8}C{S-Ox-vbbprAVw`Lr4CuxBdZeHKi*(;owc8m6iz-i_Dhc%n=kq=aw|<ye
zN)7><(q>T*MRSV08>^VP!L_yDIV?}yqYbqTS~F&Zeqo6_CfcmtAj_#vZv02>Vs4@g
zxm9c^hd4C;rf4EmJRa`yc^qCsC#?BG#GDO8VAR^3oCl~EMvs88E;)CEH{1#SlO9&B
z{TsG!!MH8sB@W}drwfE7t70Am7maNAx(E8qt+QMTd9eR)D^S%yiKmBarqF$y^oZDM
zT<?CD{*trOzVF$7r*hDvYmLYcHLRHIWIXF>q&E(VRSiD172k~HX_$&l?#tld?6Gt6
zYmD$N4T~~=Q;pe!@hTiq9lF&LCCng_o$Q%{uO-(TMUP!0c@|>><^cUyw9Bif^zAAE
zt(<*}_Fr53v(U2Uzyqin@{xD_nnzO;Boc$qv4{O|P@K&LZ_q9R47ffRI2mVRsX6=W
zi34H0s)k_LTNt=9pdF;3VAOa8l)KJkOd}ape0p*T6{tb+hbvkn$F9~@QlV&jIhf96
zDH}~j&RpAo_fcQsIZ;W-ds5+ig!N;ELPfgv-6&dU!Z$j%k4eq6k45>%*SW4pg5MEv
z4J`ohfgRU!4HTWvu%<Uyht>L7%}enx1i=fgaRR?k6S`S^2Hejq4No<UrJA+Ucf1s>
zOgn^U-L-;Q6&{0-<5Nn!XW@dXDjvF?9z9=0fBfW(w4&I}z&@~pT9U>Tt{V%WPSQ^4
z{NBq<xJyB<U7c3>z4hYF(7f=Y?Ol`Y{rIhSRs_6#;P4?Bx|(jjHcD>hq%mG^89eBH
zIr4XhsZ7z$Z8qKLiH4ESV*<aC*I_U_gmt{_e8|-k59~7Cgg*E&*R~d=SNWtLA@{9z
z)oL=zc6GbloHjv9re<B6DQ}gOe_1xx785Rm*+Xf5&*DaAk{+2|xT?0PkZZiT1;kHb
z${9==CY!zA?0(Xu3e3b1d93IN!7nAV?6&YD&=$pkJquj6uK5N%sCRZYF$-y{wgJ{f
z!WL6s^oPId^+7a#wwN)UuV2Pf;eY>@R1cbeW3oQI?cP`MQYQB!y{XG==T9DGh2@T^
zuj3W3bij>T7=wPZSg}c-G9J9$*MurHGbFluE}c^5Bug6l7ch&KiGwJcB#Q3*TAD1?
zX~z3gdZ-z5vCK_1LkwwOXI>GZCK+4nDl+2oEX?)nFG8kSbzn;~1$297T6CBIEn4f+
zTAuJAeLv}kK}YzN1A$RH`8HDQa6yQ>HxklSakkY0Xag{jQ^hw~XY2b8QM367k98A-
z!?UgPgX!s50%1w<)pTr7A^iDJz+ReHk|Uz}P^ypaOio>;Eu4nff{KyZ5&=}8qSo_y
zSJU@I7Us_qScLg0WoR*Sf2|T(%(yEHNz|8wKeHj80nT|`iO~8mqHV2nwHF?}Z!Q5n
zR6GbIP7noBO`QhKV>Wt-rWB!X^ij*;6QGhg)wCC(#~jGV5oudS_<>XRXAUIO@e24{
z4eKV)v7Ipq-gJDDTCzXM&yZ$m$?p(rit)_Z=SCX0YBzM(loYImg$O1HHjq9&@xHrs
z(@AXkeGsru$BT_{nGzQ53!t8_Fl`5#GjlM#5y@v+GvKBtD|lfkXD3MP!WWJpCX^qO
z%Ru|q2|_|g5ifSP)Q*KJ#*emL+kWMwQ+mHJ5iI_imZ7tp7AtaK7Ydlb^%~A_jdH|~
zmPt}0Sy>qGvw?ZUC+D_dBJ$4&xeqA~$A&`%ZRmyYSvJ9cirCpKs&?b4som=R(H9pP
z*8`i@OISILRAN`9{sVyob$J~+x%lxiII_b{c^gnF!D8y1-s;cG%IjS&v(EdR23_xq
zd2zC#Fq#-l3#lfk*<w$~AhQs?%*u2rI9Z!ly5!Eg!8C14{kv%HoKHTMdshP0Ei$bv
z^G>x8@9z4_UwgZ=%iL)ILek+oo13!#58qqIsyEvKwOkISyX&moC#S)(ySUW4K3<yU
zGnlC2?C#Uxt>atC-?&*92OF=sN($yL)63Cq{K^TPc@+FRV;*n4sSMtS!p?5bm3%|z
z_<Z^m?OcR@bJ$Kt)}<VBtdS8}O$dkLaWA$Lx?GP%m%<@<&mp;-I~<h1%zH`}=5P%o
zn}9~1gn51!by(Q|<GRwsDWFmE6S<sBQx}0Gg5>tvwmPya5rfIk;1j$)Zaqy`KtVow
z|7f$7u@;o!RM?BUTBXr`VpPsis4I5wsfLFoPI}m^bqV1C-{<e^=(VN!N6wM=JkAqz
zw0Aedp-aq9IJ0+W*bxTjwL{<H+#wMr7|FE~rX4J+`QRs%J0eSk4`J0A`|#4D`&UFw
z-`bzUKoj4gv;f7W_66$oE7SnA5JA7NGq#L}*7V7tpBO5t?M$@LuZK<5`QK#ns6UZE
zGO#?#Z$hQd`1X>yNWfy8!cDAZ^rhRKSYg|16YEn@YP;##)wGhsWszMTFt=@RoXNXl
z6LSW*L99x{N5`J#`*|LGhZL*Ctk`V$Q|TV0XvlQ&b)rcahdc|N2@iNg2n|{XS%|Z|
z&fQKFXCqOzb5?CuwK26L-U?}?gQ;l~%J!5d3`9s!JH)|+qhce(qN|4a8+V^Ua}0~h
zGxM1zNc{wsRLf_j##<@uC@Xu>?mK^#L*ZQMs}@!DN7Jc8BQi9l2g+`4T(AsDZ$D~s
zh}8zNU!X4pDmY~cZ$^aau>@1hT+9zvI{Oncmvj+bDmbO=hFmGVC!{XySW|wxFGR)X
z_M-ij?Md}RVDKQ=^`{i%36H)jPrQd46uoJOoi(xd77Cyp(nmhwgqa4n)+j6~qx_|W
z4fr4XF=_!EZVc%6RRIWHb`gJ5HiQ1XRSD|oHs3n_>UV{DnptO31+_Z>kHhxJEFl;}
z#oDU&tNNdu_!i|?4*Z8&I8tb6?M`$fY{c_pfm+yu@>uHw$Y{37u;SgdWEtNvvlE%V
zUQSO6oS+FT`ZEX`IsrbvnD*4Pt2ID#%dEAhbek&WY5*ZYLz?ccRr0t!%?J%c8)nYr
z7fcwO_(Vwsure}%b$Co7W7nB^N09fOxZM4GHaZtZod)y!+0PJF%8()3;{pEDS$0}@
zhq!fXqRT-$nw~2}-^1T`>3sh7XKt7eb6M-)8*`(!{S0^@1_Azjjr;_{@D?hD$fPmN
z;kd06dKY~(te1R}hGT=Um;?F;%-JRVyx{kl1v2;3zbk!$ez_yuOP1v@j#S>uEhBax
z{n4;YY8JVVy%f@VBvyEOSVW?!cav}19=^mcV}~RM4|x!i8N_8kLiM=$#g~;tz2u5-
z0@e*u=%H~Z_e$yFc`f7li7@ihe6=B)dMekV$HTD1zVVeXa&l=2N}S_;9jlfkg5h)@
zBx=9d^?-^5#p|M-$i88vg>5J2D0L*{{Z79hWL+`t3<Gh~Lo1<1`jn1OSr1}<19dO7
zY)|u5M@Q4C#K-_$`lwl*fpFfnB>AYh{Kf1GY?<eAh4J&Rjan>akRV)0VR`?SHOKs{
zcDzDOdUbB<LvRn~%GlWx0pU97ZicbzJ{Qz+A}s5JWLW_@5bW2}XwgH7^LQgUHsnoF
z+PW}P?LYoU-?aU6xc-3izT^+xu+^=7OQmPW{`q^wp~l0|h#s~s*?4Ps#Oy6ZbiV;9
zs3oxq?zT76f!T9$HYYm0az2}>*lXonSxlu*1+`mUYjjU6B1l2dO;bS8wz_b|9Q4)<
z)T~>o-jA0S%371N=~-16>rc@$h&?-8f>P(rp!KHfYUDgile_+U7j?RhE6Q~JCQ;_y
z47e6#IX3u_P^77s>|MP&@+<DL<YL44rk~yC`v>`NtM#0LYLalfKojJC*+cc4(g)wk
zZ$js$**ZjBz&)CvGKF(R+ye;#a2(k*i^DiXAUsRY${ijDa`8{vWb@5hNoW|U5hX5*
z&k|Ms<o*H~GQ`iHErd~i^S4i#G<Um}_QICErr|GPIJdN)0$MP-iDGFrb(|HT8c=YM
zyhfG$r&|Q)ZT{MfLa}~<503d4PK7_4(HE$R4Bl}I_$Mw1ML%D~LRyHT^>J5EG{4%(
zL!l+AY{MtfMTGq@fP_F$P>iLqW0@P~43be&7jA2IwPCz(2iJIWps{JH$c$P=>$vmc
zck%sd*(zkBnH;=*$GkNC*`J2w^i+nXn3ehzgS|W;A{mFKi+|IKX^J~IAoA;&&VHLx
zn_13-e865V>ANHng;E^L*LlT=a7}C?`>bPP*!Hq*Lr;`|vm9&<^fIa;;BFL}%%>W7
zgtZ481GO+_6%2Rd3T&eN-v#6K#rdsJfA~{BsV`AZ2$!^yaS9`l4V;-UeSxY?-JGYe
zlIsuXNYGjk2))u{VxU#5#67h81Z#6~#hzM}**q2kkF+W^-6Yd?tj_ucjrU!^*xeX9
zU^rNUZ8zBRIQE#TY`H7HlrJ9Poqd+HOD;1N$Jek!rKnDt4u5I38BU^V$rRP&-uWV@
z=~<d{hS<)bU#zDo;LG?)L8K)`qNPEf)2Lgd&Z@)PieP!iV3h=q4CUAZ;>h{uJG{`g
ze}V)j`@LtaoT1q&{`wbkxEKbm&yKMZ-iXdGI-%lqFCRFL44Xwsx<e-Xn0~m;=R~~|
zodZGQ*e3WYM>zWlek;_jRZ6Yr(c7MvG6ZBkMD~EC3Qf-4!=#>QbfV%ABYPn&B<REk
zKR0@PCy}KBUcY7k5|A9xJ#wlSzMNa3=#$Nb<k2{6q^)VKb<ZY1q8xqYR}GY4)y>0e
z$^7v?)7_wMLfmZ^dsu?mCyY)XklgQ7@=p>1UhsmZ&r51^){-YP){sc4ru@>Cc<5Xi
zARmlqkPur>eXxrzZSmG`b)M;alg0^>9zyCE;U^NdCybs<r`i57H7(vswxmN1F}i|g
z*@R}yMBViy{rG`pwuRk@Y99B{34NH*A-5en20U}%fg7zFrG{+2^F)w_FjF|?YH=+%
zjr6Svf66#JmYWHBqM;&_mjmU{6l!9~HD>7e8wdZe$4Bu{gG3MOR=dF;s|d&0C08?k
z5;<-Aw(&qU;}~|AzTlfzH*cfA`ZQl2kb8eCa1;jv_m{5>^&3t}?$a0Nw91h+ZDC+(
z+}n2evc&5A#<%(fb2!ZTDrWs|J3Ha0k2AS=W|rE}vPGLdzhZD1s;9`WW}{ZV??_v7
zLhI4%eGCeL?4)NM7{{BBc)W^vq8D9BPqTz%mdg=qI9m*^;n&sJ`i&UTFz@hPH_gyx
zWzk09<2`?H;t5ev{bai_W(Gsz|A6JgL}gE-GfUjw@M^n1bVk*)zYJi5X*sl=B0UHs
znmLC)M9@j|3&J~MOJdr%hA2QzIK%Azillwb8lIT>6)_Ow{msskGPFlmn|bP_cFK!b
z*p$h;d!auw$d3F<R(vG%%El)wW@2bJ<P}jaaeM6~ZJ%MnjPvUzLh<ZjXArWen%{Tk
z`YGbWUr?v)Mt>x`sc8L37BbA(r!UOD9dg%>bv~Un4$lDgdevUAUYa^PLYiY`;E}qG
zewBxG@*CMTs$ISuf;%U`6qwuh5*|Su>BO@gGC}a~p)3Dc*yQK*B{>V&J)s~X>YpJP
zG9If+f^Ii|F_k-@>v8NUaC4BL#b9xys+*Pf+x1K^qu*7X$3NGLtH5}7Zp^G-7q^4j
zEaKmJIIwI~0?SRjp(sy$u=y1hFJMKB6{uMnshaTfwMg~3f_h5UXIGA&o;6&T#n|1N
z@sCeKuE9JN$hlB(wPwl8&z>Q#;x~b!uU&LuF+HnS$WR|<yzYg3eKj7!RoodLsSDAX
zBStN_9cvbTLq;!>>Sy02wFbP3$$o>3lg(m~sCXrmBDSloOtq1wB+^1$|8O@eJqgiK
zTeA)l!FzT74UX}!PJT7INnO?$_AR2B$$o(1TCz?y(x0$IE#-|bzSKF%X$ns@?OaWn
zx+h?wUATldZ{?!$0yP?bUMlxtQ7Vx@jdvV@v9hM2pcpiU7jdV9^qF@M#Q_C#e?Srp
zoh%S^g3AAy)EaZ+h+{Q@gfxq;QPQQyq3K%<p6L}a4M#B?#m-m65<reRN(Mp-c>3u^
zW!(<!;V}ds<A}dV!osH<;cHtI8*zOt7pLorB-bwr4|^_JgF^O(nL`1ez!#&Mne+I}
zSR>p+_QJknu9r8c!F#dp*lhaE7`$^Xe9gcSQ@Rq{#k4P5+hgzrX+@_SkclR-6Czh_
zo~=*oOxr}ck?G{aU<hyw7Z-<e>w)SpsRr&#_)l}9^()_#CD@xwExr?1PxCz*Qyui}
zK9xBzFskwyG`1(e=HZa5x1clRn#YC<Rq_Q>jyYLn#K%zzH^`aaIeOxtcq9`_p$2n_
z`m0@okxb#oKC~)6P>kClXGMYPrF4yqHPb8*79EMusZVA0kiP|p)`wuc;J`HDI2L8m
z&3oCd4#@|;9*siQ=6;bDAR}OvZ(UewOr(PX>_>z&=EK)Vqt=gqC;_)dd;rNcau)pr
zV9yBVuXHi30@emsuh{|`90*YFb26%a3BGyzmul5Q2nLglwvtPZjvsk3DSPd+qFrV%
z4OTWz6t5=@=k1*#H!_N2iCV1T6Z9e4ddSb+(vU~o)86ZYn~A2`{qbed|FNuzkOCQ=
zXnsratzmbTOZOZ?rQHeTDY<<lgzO80U8bn_T@!gj8Xs&8A*{yJ<jb>ahlt<Hv^0<U
zWrh?A1F|@8ZpA{t1HVLIDQ!w%yChT$J%*7e@;)yd=j|3)M1~U%Og6;bZ{1J$GE+@^
z5cM4eNU4t_pGtHio;ro8!`ZNld{ekny-Jr|mL3A;jYaPhY=x+F9~sA;x<fP=GfB<-
z&#49D_sNO{T2WkQSZVX`eH(#7A4y)f69!gFk<z1wqrJ8k=cNy@JLB0QC6g2u^~BuY
zJER2Kl4XnPkb54PNQK~JoSJB`w2`gdhrS95TzICdxVh;yO2<c_n)mJs?i5#j-Nvc>
zTvbfAK9C+_(z13SogA#N5dP>&st}rRuBXDOb6@pcI3bi6glry;I%m!mPN618R6;tW
zKF^R&H-tPwS5L!VJ}?eB6c^661n;T2WME&G4#$NOq<$AFAeQY_t!mn6vZPqGI1jj?
zC`2>Cl-uO?wi+#`ky}+KU6_zfco2CDQK}y9U2$i*e4WUfqqvmy-os2h>b3nttPBu0
zWfy6LD%RYn8(NpCVc_FxxMw?p=F?XXiICAcbNm$@Ge1fvkVq%KeHTk5kF{}XnB6S*
zkPfXAzZD|fmDqtRT)hdpE7Du^_-rzzX7*Zdfh%C}>`lZ>K*iiliprpJkRQT}7^Ag3
zV)RLW{ad`fydrBZC2p5wqo01aarA`XXWJ@V>Cj`&&0GN}a|v|;{8>Kc{HQA7$YO4?
z2_0=j8lB-Dg5J`R<&#~}wOhti%*$)1*96pcYT#F2xq~MOr|)cGc-GnYyMnC;nXkar
z6vA{OkNuq&r^j=T3kTj2)E(D8++VNRs+ZUnT8^@VqKm*5XaEA+w*mcQQKb3+Zpa$3
zscy@o6gp8@1^zRqfIM;yM6K<IvXcFXKR$x^rSB6OD!}rKA)az(|6b~C@S?-5IQm0i
z%>eK%o^-TOhuzm(M=gc&DW9aBfxRpvUDIzd0rRkS-EZQQk#1AIO#=o2!QWU8uI22b
zq9}iyh7{*vry$f@xZICAL1*!`{&<XoK%6IaY5A^-No#$1!l}iD-%Ck2FU{F6)ENx9
zHinTtsAg&8r=YQgp3uo6qR4#`*guJPd>v4NnHzYi-c=vb$ZZ3aCe9W=Eg~V7RX?>B
zYY%~nBAHP%eCw|b%YaaUsteOZ%L-ii9U!?TR{yMgR55-)TaF=^rN~cPGxVEl*fW=^
zy|GDqK7103J1+W*{&S4$-=;Sb1qI=?hZxfxBxhGk>1Ntry`89pF^+U(f^{==0t>DL
z)Ddux5uiXH7oJrYX%#vnU`=G<$v$extq-rY6uPPAVu<^dtS|u1a9G&BP5zsX`9)rZ
zdXI}~0i5*CfkbPsc1f`LTtWzI7753U;1ys@tH}Fp8O6DKZi23=7^QZK*moT`#BUg4
zV)(^MuKue(M8+by)y<ywxzRFt=<#5+6r#FW9nu;;E1ocFknnEr#C>f(2I>ez)7xY6
zC+<Z-vOebrUSp+@)RO${MylKt7qL~rxw$DFH(so$KB#|UC4?8|uwa}jFR~LmEt&r~
z*P-)Ql?uU4##4?-p*v|n)C^D+=iP$&k*V0sKe=~Z=xI&pO6%>?yoE2P>XhgGyD~{I
zROn`0HUyGa^$fSGf#j2x%u)#=sp4eIY>S3!1s_`8cLUYykX+j9`1#1bI_vDxBp-(3
zIQC~yHO$na$KWRZ5?e?RB%;6%@{5n}Lk%7(-qpd9s^td_<94@^<+pC^oGNj9zquRf
z-dq8-hf^=8H?R<_!Fu{Zc?hsI9stlKoO*AB$a519RK@Vk*(RLCE2nloBgUaYW)qMQ
za6FJWzlYUT&vrY?OP^FdRV30B?fH#4C_NTonL*P~(&xFxV#3LA0{7s48m8v=ca}#S
z%t8%x6yfof(6u|rGsy=5XB>&9WIW4gLl7*X%y==U!Z*f>_Cun9i&xdJ*eRN?Z3k$d
zGM|@M=*L;)jMR5&I+IM}rc`gsL^S|%Z<7xZ3+Im4{MO-w!V2I9p>q^Md5)Srb>y>m
z^o|O?xtr69-mFv$AfCC4Jr1>r5ySPKQ+*>L0@Je9%xSrC<s0htP>(P7Pstp}YbY!N
z;dn>C=XYfoTB!U1M@Sk9NfbllmGPX=&U4jXW}&h?0d`Qo#Zl<MMxB<x0;>mitVBU-
zQ==bT%rhE|KSR#A6%b9DC6j_1HwUX|KNR^3a?tpx;yp=vy3EHnI~YEMU0+w!SA8ZI
zMPk~5S?+?p2XC3J<kjvcpzyqoSrSQSxI40)E*$cPL3!+^d)CoA!mW9;M<jXbWDTIw
zQ&Aqw!-xZ~iOQ$$ChJ@zB2b8$Y!h}oZj&-qYP59A2-y6%#S%{NE-qK#`TeeYspqZr
zg5P|r+V~iUB5Gu1TynNV7QzX^>GI(mn5&3ntP`NEnh(QtX^S|l(1N=F0B{50Obh99
zVT%koXwuJry`^Rj?}hf=p*2>=!vg&iAxCJ2l;ojYCUt5kFX7@NC+}F#^oV@!4Fxxw
z4*QcYyk5SqTv1luuF!x_2C^^+Qx#n$y+T5n-q;4?u%L%qt2yov1<6bqTgq2PA&YBA
z_S(SqpeneR!;j(@AsD6F?;F*yUeZal%Fs&%G+m-#Evd%(-6kPWvU1Vw+>H)??Uk+I
zlr^&sylp8sQ~^NYsZG&$QQ4C~zUv<ehLUUXt*A=_*UH2QRv!&;^H}a-Bs}d|Ut^i9
z`e&3|K(wow$lDVwv1ap}`K={I&D+x3ClTQG<nMkomP92fNa8qK*WB|5eOz4fmuajh
zfo)an^76<Dj_u<{TTnWY*nA9j9^(*=MXO1&6EQx46M)hlqxvMtQ-Tr+nJ@~nEW;pg
z{}7idStPucqjs9eBNyewE(Oe&-{ajh0I2eS+csFg6h@7>Y^pkzCEc-A$T16f*c6gq
z;cujuYKMhw6%w$dFghiNXu1m@<P~BDK!`D9UPt#GT&Nq2Q@WE;=j=|7WZhyHJRMLD
zlbo5d-+X!bRoy3e5_@?X#xHjn7g(BbGGSWffhhp@@OaG5trY}M9~&@)<uc&tK<%7k
ze<K|&Z9-mpevJ_?U)Atg4%kfw6L$iK+i!%80SG}S({`X9mBT8ax0T3fmX?@fr&Zzg
zAXB9xxl#-#*lcy{j#d4TPwDwn=P5LNGqttS<1Q=Q=ct#%o;j}k)4!y$O(m3wJvb4|
z^<&eLgir<}1I<{yRf$n1s?_zwHHVysrjDCwE4e%xICR)XUpaapFu=?FZE`anp{&m`
z&|96{WX=byGJ~p#tt}0zn)Wb0vVOi5lHrQdA1MbRM3Ge;fUPE;na9@ZP22CZk{gqs
zWUjbm+&`gbvJ=-X%x&3d`u(Egi%i6ZYCO|7Mb`k-&`#>F*ySfU!!)aQMSyB{EOXP7
zVo?Q&5&ftRKuJBf<3GFcq+MiX!GAK3>zu05ARY{E=+Dr26Y(D!Sx^oL)7!%Q_<V&B
zZ8|BmJ?Q;?Hw%(t*daSM*D5sR+@v`5AgDj!MNBFviB<+4e4NBTM*A%N3CIc<DeXZ9
zWDoy2B=ItrFcrmCjfFOrWl|CA2L&;cNNrF#k~u@l=`@tbq{B!(yv%#U8Z}@1+F|sI
z6=!J>=UE*l48i!fth%2sr+Zw;yK7)4!k<RMVM{T<2P)Vc((~ICi^fYVG5Fq}3FyG6
zPywk;Xh&xGHu%xhhnE%BclxKYuv@bgwqCgmwX@!eX7%r*+lwGjuY7X*n!$KSaI0OV
zDgi!K{exz~%-jhX`Qo5uP3=1F6pkw5Rfaf2%f)G7`JfzcJ^;3N^~;eV<@;aYO?ix&
z6A7YrrTA%*vnc_N7TkPpE6YMR$xq%aC>8=Cx@J-4C4asY7)=X%U4~ms;T?P5LOPRz
z6_0MaT`((RiB&`#{U!q0+?N|AbO2r<d<f@;0#BgGp8z;+`AUB<^DPpVMRt-DC1&g?
z{C%xXk1w{IgJsq{Z}t%YG7UqE*5&Zo<hD5C7xV?rwcIB8!Y-Dv#<&m<jF&!2ufC@i
zombr|J@>eh2C2#61An_`J^y9F<L`J-Ovlf({)7q~<y^JrpN@r7BIDF3vYw=X>-~xt
zR9DUdej2$MX6u^FaklBppdZB*o2YP?zGX0)Jcz*q?zE{M2lt+t19!M~P&!3VF#^rk
zxEZP>%xA`btc>LFIj+Tm6#A>h^O4$Th$RRlWc#lLBN222pk#l2-DqL|elAA5Bgu93
z5&vxrq1}=PVE)X3aF!x#n)i$JdrFi^yJ(PmGuwUZTIugU5y8esbFpH><fE9Pm~!c0
zk!eP>PQe^~`iqH+7>Z>dVg;-GAZ6+GJx?!=!hgQY&Bi8Hi*Q}ekH@5h1pawPZu_P7
z-s5wx!#xbI^V!H8PHOUlk#<r0<p!<mPBBALay-0R*A~8o&O>_K5$mBl_Ny(L&6gem
z@gB4-DYURs$h?{dhqcVtv_?cKq1cTQMU2ZwvLEvNcXX4TpZvXdiYt(IyQX~Zm(cgf
zJkLj-0naNnE6>FF?uo#Sg|p_~n9hW-B;4HoER)k*KkfjiL>br3<i1)y=0EGK%=`GX
zek3qL?X!7YSC$#l8FsUKPh>%Nt%?7DH;40vT~c4;fJw$HX1?~=?BZ30IAs6TEC86x
zz?_D1K9a~Gu#!qyi<VmY$f7lMj$}dnC?W)-Z^98=|MKt>KIBZzb0F<zp>aA)`s9`5
zesy<#eyBI$!z(M+bLNyo@ih2TO;kI&BlJ0AFJ?Lur$@zO4z8y%`0`UCBy-Gco2%L!
z%^}~vyiGWba#M>$aAyC{@=I`E)pH>oAu_2+*B;GKT0|JCLin9c5F_U$)Di~pIeV0;
z*p?+he30@n>5UM(WXB04q9ydhM&pgyPEa-dms`92V^Cv3q~8sGG?wufWJr^!l918g
zXGIGZ`U#Gq(N{IAZSUQ7$0J2;P0Db6n7R^de)441W}?6)DYH!q+N;u3!<8pnKRkSj
zk6ur0i{tB9v*plie<f{}#LLT5m?czVcURV28Mo#}PW<V51&7qC)(6VEiy_AUwNTXL
z(KdwLL+VCow&X+ynPw;HL`=db*-mVe$c`<;+@<Y&7yQ~B86S*z6fE(#dHUhFSl;<Y
znUOY^no!{a9uyo?=oFe<3AqknrxHPBPljiGw3cOdng?2{VE(epNTPG~;hHL~RIL_H
z(&7$f6ymw*`5j6GTKk)-F{MsX%(rj8_ds)7g}U@E#5^KY{1DLq5i^+3sH@92FKg*d
zd3StNuc;a9R|IL`8DI<~x8(2B0UVY_0G@QEYv_*;o8fYKBpvBhz6K3Zr^+NzoW<Ho
z56fTT`K?)}+7u^5q}<9e1u65_C6WH|KI%W!G6^RTkQhIWQ0^WYMbg_x8<%dec(|1)
zt(-PTN-k*vtdwA#eYuS-#uS2Ds^uVqW4>$a>B4v_w3$L)wwWoHa*%ACD))pMM2FG%
zW4E0l<EG|7mHnW7L|-nAXgRjtqCU1xdSrEXWph*14_Df1G?0p(e5}Jq)qQkp<61Al
z^W^%{iFT%CLai6;kpQTwTL<AIXMx8~4wCYMA2@uy)XC4N&qy0jLfFwIQ%Y`~6fas5
z8f@)hw`Z%O3~gp@Te~p9qn}!QD<;2iBbv_iS4eV?dY*(smh9OMiXjng^^T+(4-G<x
z<cgjjjBCec<se~-Kofk?`58@o1=_EO7?ER260q54H8rMghcV9zy+Sp*Yk3?ok;MlN
z6SuP<IyX)=kMXS{P1S92jtohrE{bIJOyj-4Q21cnyek90J7sH=u+~VLPL;gTV~)Nv
zXx{fHu{{rX-nu6{`p<U3j1@$%-{5scauEP$T~H#6K5HV39n>F%lYwM<%M)+JgTG`k
zsf`g%G+YY^;(Rm+HfZx$?N((p?Xinknvlv}(a^(c`&}yUZNf@R&kXRyXmS2{#vL%!
zO*E~VLXs$&w|s^VT5h5#Q4?<@De=aQdBbV_F0@qh7_FzpX}vMwDh|raT;6ADnTlVc
z$Hh>yH@M6JZtU&}Cy`bCEW}ewfbshi1hze=!Jyw;RuL1N*Ds61rbBk5IIkI+HPdMb
zmwXtiHDzf2*1%OQeFnPccqL&YzfptsOuU8XfZp&itBGO1jJu}2WhIcu1;?kQ%E%po
z`6F2H$$dS0OxJpoipe<jca|~_#}_Sdici<t_G{C2@Cn}ir7v-(QIcru8>S6!=9b;~
zEMFUb^W`Rzc@|iya-(Gp+%}oq>aga=_`&u-<H@qz=XsnT=y%QpvWMNo%9bq2Xf+_j
zahy@$9%`u2SrsElorNf}5mYh3)Se(F=|jz1fo{WP_P=Qin@UGuf&JR-`s$}-YF%nh
z?yTVSqfEJS*C^slG>2}ra0EfpV-N-QtFEavGAxY~^+?CX6urBgu7m-PhxC_W3@`bR
zcB<Sp-r8{^c}x}RE|qh)&!bpyAy>!vqqWEMUo(M}1;Ze#hRh@~zmy3-(^b2gw!dW`
z2)@RBKqZv*G^uCBf3s-!H->E|r|fps(r&ELUdB$4D!)^K07a+S$zS-)m9YgYK+FIx
z=3<cCvcEU{I3T;7<KX$Lfc3A55FsSwZW+<i`@LT%OYB`+bTv@RrqS-q50`WG{@fjg
z>XS4bzRZJ%j9@cwIY~;h%4%FKEHw`)QuE|#5N=IBghwF$!H1>2Nmt4s!H9fwyt+Lh
zAKl(!#4(>=D}ERd{E&^T5loW*e&A><yA|dotiNxb!fPsM6q+Jk9rkq`frkg<%6qY&
z=3Jf>EAGUY-?WKZ74tl$UznLu*QKLT7r#X8`w7hnJA$Jamy&UY7pd(a98})DuD>VI
zh1nkoUrSHXFt7VTK_2Jb(ruf7Ovah*eVF0q=ho0eb3I2)+k@nQm{eK<>Z8Cr;&->s
z1cx7ZSMdk7p_uuMTg5-&emiF)A?IWqrSOBlLxxCnca|U{EoJXHOhGL@oDh-)ozeB+
zZ+W1T-E67Nol;OK463dU9Eiu)JM)$$+0<<%^QXv=^mJ0iMG>1?oAk*|mK}MF!lx9f
zY1P|~A<96LQYzA6XZaD3^{hS{pTyM+bz-`ulut(AHqrWq*LIH<L*lE<kbabtVMIHD
z`qevNDE^jt<t%q%(wpvC{ML}%mc6c{JW;K;b<zI3ONOPYr$pK<L=TFj^wF+XjQb{*
zQkB>XrfU!)Fyb@I%3qd__kQB9$<EVpOtyfspe$2p#&6n|-yiT<Z2wksv7_P-LB-<=
zFRRfRh$URV{Kg^Sv8)`~_ERg`jPNRNn79i7!pk>y#wZ2JU&%Coq5TY&o7><LTb5jO
z`&+MT$9Ctj+t&3vn8{Iom^Mjcs6Iqiwv(!o{u}bL7PpexOK#)#ulS5b2A*w1Y)6bU
z@g}=)OGnQna)4+<Y*<wI0(W<M9ywv<j2GW;S?18n7DryI0W-mT`16UW0HhDu?+2+t
z=or!h<nm>x5D!A9@kxjI5|LK1P(S_Jw?QE?1Z4LR>-~&;5JLO>91krdO}BRQUKSF-
zVqLy&jL@rOfn3!if;#AxN^ws{FJ{j>3oU89ailmPd3`%=*hQqmAF42XeD*UsHDMh9
zA;C>^jB&9lcyb74lvB@@7pS3hwDfSsnB|v0rBDxXedLBw%?frfKjB5fMal3-bLD`V
z!lV_%13&76zpLGc5dNC<t%o$_okCIPiRxh8fmtzjj_2@kQSzjq{NrG*xTEyb=Ey?G
zGXG4*TOyY?_+_lBrVOVbl-Pg(c4tC_mC`}>Sv|^pVX5@JX9CO`3uN35Xa~N@+<OXq
zVGd3xoLLFHv$x5RLzff${HRC;eC2qS+9fvc|8ARc2r{#eaV`YN>E@owrt|EJy8;{@
z;@sh&lY;P;L<@4SCAGB4CmWT={*m4!Kbb4!I60Oi@S(4~rpfkD5)=_=%$n=f%D4Dp
zmt{GKsb}rWfFi)=>-Z+PEnR;WZ}(mGU2+&x^XR=edav*KOvMh_Nm=(j;d%d>&INB(
z6ny!Yxmf}-bBj$=bqNWS5Qr((vFt7+Klt~rxFDLhczhqlKGPJ?l9fDCS4Q<<Ao_^v
zxU>tJ4mdCGPg4_IP-w%$Mz;pHY8Wg<aW@7Cb(?XeptlsUEhOT@7`)GG2E8KXDot)y
zanIgl`g1T&_iWt#ZzvLA!qJWM3NN%@IjT<)*HOKpJ2j}B&$hsB<Kao<zo_GQ`o(&i
zdZ&@KUSu`Vu1;>)Ba}$o{^2CWu0+vS@q$6DHr7Ii%{zU#I-O^|uX!w}`DDugL{Z<=
zELLZIpam&+?-0MqLWWu%`9MP6G=rb$L~aUr@~oPGdD-l|P<Qqu2#;twOL+ddsx@jU
z^~97$8(ZWwF4f+CDe7<j#ts_-I7EM+&5~XCoE44=7zYu)%@^5IRDbE_Bs@w5YZufn
zPfM6hR%$u3sUHghnbQ3drmrySAZ;3`fI@D9_fSPmx><4aD|NJCTx&R|9WqR0EXKeL
z6LIEfrGx!ilF;5|hGcAPIjel{bDwZ6)kQgDqLC1zOEgOyjlfF$&&T4Dn(V}RdUN8M
zSu<xNcuw_<WX|kMY_{rEWqY0KxAuw?GX8YK`!o14{9T6eL@aQ4@D!6_4KNm6;$*z9
zj277`)G!cgqVS2wUWwMY5P_Win|Y)V@nHqb?F$umbP*Q#fpE3e?I}tFYSn^cNkjR2
zuA)sg!$zXf4Xz|0OO<3iJ(LU5@9<oSWOO*UHQJK$5XvvNE$ejR{VZ`Y0@0}pPd_CU
z)%u=PvP8DL^kBFI+f9OKzvzF=h+67J<DY|qHooK!Zh-QW5<ox2gZc%rE|h^dHk{fQ
zizhXBIrSTN(!gaAJnJWx#Hw{lCu~<u7M+DIqK{TUs+VAk^r{F&DxJRqn8B)>#V&{u
z&h(_cn{~JfLYFqfM3)w?jL$i!X_&@*vPngS0g^=>t!phfWlP63wXX`&zEE7cc!Ma=
zaN?mPjV4mdYZ%`e!aFXB((8LU)$NQPoMK!Ve+?-aQ@s*Ue#mR~Jn@>`h94c?HnRe~
zxM0`x=W*Wo7tKlP)4~2ur{fkQ@Jy};Wo1$*EqY1tI}AxsLmb9fmrEW?LrZTc%~?1q
zCCtR_;S-UdqXkn<ka-AFvz{1mla%INhughA$RIud)%nan!mPPlIg7)?aAwL<6J*U{
zV$U*^avbz$3C9kId;G>7m)e^GJVk)1faH$-{VB&+7#62$8lT0FQIvcaf}9@of5Xk4
zJlf;Fj)%60@i1)rQYPeWDS1#8>5SzeP}^82&lyTC2|ulN;JiHZth=H?Q+5Ojuc@+Q
zj^3493s0{-Rro2%`4*<Kyl9vC+e{G_i#|tkWY*~ALZw0)blETnb8DIi>AK-uq+Q#s
zU(Z1%RmtvJD?75P-d;o^vTL`;_9*gTUV4rt_`IQ>?kcTeCATl@3Bk#{hVZ7?PmHxL
zpXhi5hSF@Nkh3zhA1NrTkT=0=GS0yE%a8$QG?9DVK;#*u$mIuSW{coVkaOdgRDGCj
z(M%BZ0i=!A{R5mD{U0-tl+x8O$4AU@9Y`ByAT*S?uEQY$A@42N-t)mZxdK9r42}Y`
znz<26Nw7DljW91eWHo<Evb*=l@GHWu+_L=k?FF8%#YDtM$TY@8A9TWKZrs}t-%T0N
z&tOOE_UW)cbj?F~F<t?itY8#?{X@aYA3Woyf*fV2?gfIjgtHuJ0!pg<Jp+%#J3EDo
zesi7YN)RpNx3ORj00pJp=zKPL`6>&s<~gy)U|+&`>utyx$$=Vh!Wt)+frK#a&Xw2E
zX~A&E&!x{}kuTA_YgQ;fZa1IcuPK*b{Um?f*rjF~NFh9t&uBCJ%oV0*WoT)1K>`D`
zyN~b5_55xdwrLD$ynfu!tNl#>67i7!;eM_-ZC|R_tTg$PQGkLhX3>q#j<NN9z44LJ
zP`H>1xP>A0dmo#wt+!&e^JN_L+V+aMk8jP4Tyo}cMzfh{b>lZ-QMhSp!pmtoXsXZY
z0*KODUKdzf+GLw&5L>z}Q5AQ+BXg4S`IC4oCbhscj=BrrpYn0>e7z;D6)@D39G4P`
zBA%k-oL3}X$LX1yj=kx;u|fEUnO=?SaJn)UmTr32)CENi72?I?8x(L78SM=0a2LS?
z>t#GX-*$;p;FDY!yMwa7O<O=;S;)@1X3}2N#|0_CesRv!$bQp%$Qk!q5<=xkm!|6L
z@$YtFr%kX+V6nBWxcyKrWzA{S|0AU`X6O8~gosw>5#DF<Q{tGa#TQVZxBOhII+g3L
z0AV^Hr0cL%ps*bJ)@?sc_5g7c&oM{B(J?-j=VT$~Ap5;IJng4F?l&&sKH##dv9+^v
z1pojxwyqYYK>Qy7b_Dd7nGOH}{Qdq9{9h4Z^8bnp{g34Tivk5Y-CPCwPP7Ixb)BvM
zDHG_g@W0{T+JNQ$Z}?y3{J+Wt^#@kx_dglQzL`2Z0V(`%HcmiCpZ@?*!(Y9~|E@FW
zS6fp{BOr&!*7Se3oeG$&2~ZpSCm+3qsg2`56!7rZh39|Kf3Z3+ZYO2*-PGpWUm1{B
zpi@UPAc5{``;XH9tTvhHzj*kjK+lnXGXvo-AKt~$hUi~3NQaZNu?>)}b8>e2uL1ch
zllRw)DG>jK|J3<U0;oT**-}9AZ}<x!=yE`L4rT^c24<iKP#a6*zurdw#rxM0`x9vL
z2Ve(EiGr8{Fh6$y*ycc7y(W~;UjUpKe+vNvz#?JNr-A?=El&3)NP6F&p8jS=2YR*q
zSD%6uvH<=D007&``Jev&%l}(FIACA@rO|=S_&W}N;cxoiU}5q<vOu}NX}~f02mkH&
z|BU~Q{*C{m3yA-JeC+>}#|M^e@{fjpbo^8Hzsmy%0ePPP=KV#$bbbV&4J7ap5L7O(
z<uAa{1H3W?<_8DPS+FT!C<oG{K&%0j2e!+^?%(<UwuuQCj9g5ejeuCo_S--BFPr#3
w=MHebIU3p9{~g}{20F`sO;EB5XS=@&@qvzM|0?;b6aXAVGvFWsKTJ&j2ZyIvJ^%m!

literal 0
HcmV?d00001

diff --git a/docs/tutorials/sub_cluster.ipynb b/docs/tutorials/sub_cluster.ipynb
new file mode 100644
index 0000000..aaa76c7
--- /dev/null
+++ b/docs/tutorials/sub_cluster.ipynb
@@ -0,0 +1,606 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Sub-Clusters...."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import jax\n",
+    "import jax.numpy as jnp\n",
+    "from dataclasses import dataclass\n",
+    "from functools import partial"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@partial(jax.tree_util.register_dataclass,\n",
+    "         data_fields=('alpha', 'sigma', 'K', 'z', 'mu', 'pi', 'mu_sub', 'pi_sub', 'z_sub'),\n",
+    "         meta_fields = ())\n",
+    "@dataclass\n",
+    "class Latent:\n",
+    "    alpha: float\n",
+    "    sigma: float\n",
+    "    K: int\n",
+    "    pi: jax.Array\n",
+    "    mu: jax.Array\n",
+    "    z: jax.Array\n",
+    "    pi_sub: jax.Array\n",
+    "    mu_sub: jax.Array\n",
+    "    z_sub: jax.Array\n",
+    "\n",
+    "def init_latent(key, alpha: float, sigma: float, num_data_points: int, dimension: int, K: int, K_max: int):\n",
+    "    if K > K_max:\n",
+    "        raise Exception()\n",
+    "    \n",
+    "    key, *subkeys = jax.random.split(key, 7)\n",
+    "\n",
+    "    pi = jax.random.dirichlet(subkeys[0], alpha* jnp.ones(K+1))\n",
+    "    pi = jnp.concat([pi, jnp.zeros(K_max-K)])\n",
+    "    mu = jax.random.normal(subkeys[1], shape=(K_max, dimension))\n",
+    "    z = jax.random.categorical(subkeys[2], jnp.ones(K), shape=(num_data_points,))\n",
+    "\n",
+    "    pi_sub = jax.random.dirichlet(subkeys[3], jnp.ones(2)*alpha, shape=(K_max,))\n",
+    "    mu_sub = jax.random.normal(subkeys[4], shape=(2, K_max, dimension))\n",
+    "    z_sub = jax.random.categorical(subkeys[5], jnp.array([0.5, 0.5]), shape=(num_data_points,))\n",
+    "    return Latent(alpha, sigma, K, pi, mu, z, pi_sub, mu_sub, z_sub)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gibbs_pi(key, data, latent: Latent, K: int):\n",
+    "    counts = jnp.bincount(latent.z, length=K)\n",
+    "    concentrations = jnp.concat([counts, latent.alpha * jnp.ones(1)])\n",
+    "    pi = jax.random.dirichlet(key, concentrations)\n",
+    "\n",
+    "    # this is a hack to avoid nans with jax.random.dirichlet\n",
+    "    pi = jnp.where(pi > 0, pi, 1e-4)\n",
+    "    pi = pi / jnp.sum(pi)\n",
+    "\n",
+    "    pi = jnp.concat([pi, jnp.zeros(latent.pi.shape[0] - K-1)])\n",
+    "\n",
+    "    return Latent(latent.alpha, latent.sigma, latent.K, pi, latent.mu, latent.z, latent.pi_sub, latent.mu_sub, latent.z_sub)\n",
+    "\n",
+    "def gibbs_mu(key, data_nL, latent: Latent):\n",
+    "    K_max = latent.mu.shape[0]\n",
+    "    sigma_hyper = 1.0\n",
+    "    counts = jnp.bincount(latent.z, length=K_max)\n",
+    "    sigma_sq_posterior = 1/(1 + counts / sigma_hyper**2)\n",
+    "    x_sum = jax.ops.segment_sum(data_nL, latent.z, K_max)\n",
+    "    mu_posterior = sigma_sq_posterior.reshape(-1,1) * x_sum / sigma_hyper**2 \n",
+    "    noise = jax.random.normal(key, (K_max,latent.mu.shape[1]))\n",
+    "    mu_new =  noise * jnp.sqrt(sigma_sq_posterior).reshape(-1,1) + mu_posterior\n",
+    "    return Latent(\n",
+    "        alpha=latent.alpha, \n",
+    "        sigma=latent.sigma, \n",
+    "        K=latent.K,\n",
+    "        pi=latent.pi,\n",
+    "        mu=mu_new, \n",
+    "        z=latent.z, \n",
+    "        pi_sub=latent.pi_sub,\n",
+    "        mu_sub=latent.mu_sub,\n",
+    "        z_sub=latent.z_sub,\n",
+    "        )\n",
+    "\n",
+    "def gibbs_z(key, data_nL, latent: Latent):\n",
+    "    log_pi = jnp.log(latent.pi)[:-1]\n",
+    "    log_pi = jnp.where(jnp.arange(log_pi.shape[0]) < latent.K, log_pi, -jnp.inf)\n",
+    "    def pdf(x, mu, log_pi):\n",
+    "        z_scores = (x - mu) / latent.sigma\n",
+    "        log_p = log_pi + jnp.sum(jax.scipy.stats.norm.logpdf(z_scores))\n",
+    "        return log_p\n",
+    "\n",
+    "    log_probs = jax.vmap(jax.vmap(pdf, in_axes=(None, 0, 0)), in_axes=(0, None, None))(data_nL, latent.mu, log_pi)\n",
+    "    z_new = jax.random.categorical(key, log_probs)\n",
+    "    return Latent(\n",
+    "        alpha=latent.alpha, \n",
+    "        sigma=latent.sigma, \n",
+    "        K=latent.K,\n",
+    "        pi=latent.pi,\n",
+    "        mu=latent.mu,\n",
+    "        z=z_new,\n",
+    "        pi_sub=latent.pi_sub,\n",
+    "        mu_sub=latent.mu_sub,\n",
+    "        z_sub=latent.z_sub,\n",
+    "        )\n",
+    "\n",
+    "def gibbs_pi_sub(key, data_nL, latent: Latent):\n",
+    "    K_max = latent.pi.shape[0]-1\n",
+    "    idx = latent.z + latent.z_sub * K_max\n",
+    "    counts = jax.ops.segment_sum(jnp.ones(latent.z.shape), idx, 2*K_max)\n",
+    "    counts = counts.reshape(2,K_max).T\n",
+    "    alpha = counts + latent.alpha/2\n",
+    "    pi_sub = jax.random.dirichlet(key, alpha=alpha)\n",
+    "    return Latent(\n",
+    "        alpha=latent.alpha, \n",
+    "        sigma=latent.sigma, \n",
+    "        K=latent.K,\n",
+    "        pi=latent.pi,\n",
+    "        mu=latent.mu,\n",
+    "        z=latent.z,\n",
+    "        pi_sub=pi_sub,\n",
+    "        mu_sub=latent.mu_sub,\n",
+    "        z_sub=latent.z_sub,\n",
+    "        )\n",
+    "\n",
+    "\n",
+    "def gibbs_mu_sub(key, data_nL, latent: Latent):\n",
+    "    K_max = latent.mu.shape[0]\n",
+    "    idx = latent.z + latent.z_sub * K_max\n",
+    "    counts = jax.ops.segment_sum(jnp.ones(latent.z.shape), idx, 2*K_max)\n",
+    "    sigma_hyper = 1.0\n",
+    "    sigma_sq_posterior = 1/(1 + counts / sigma_hyper**2)\n",
+    "    sigma_sq_posterior = sigma_sq_posterior.reshape(-1,1)\n",
+    "\n",
+    "    x_sum = jax.ops.segment_sum(data_nL, idx, 2*K_max)\n",
+    "    mu_posterior = sigma_sq_posterior * x_sum / sigma_hyper**2 \n",
+    "    D = latent.mu.shape[1]\n",
+    "    mu_sub_new = jax.random.normal(key, (2*K_max,D)) *jnp.sqrt(sigma_sq_posterior) + mu_posterior\n",
+    "    mu_sub_new = mu_sub_new.reshape(2,K_max, D)\n",
+    "    return Latent(\n",
+    "        latent.alpha,\n",
+    "        latent.sigma,\n",
+    "        latent.K,\n",
+    "        latent.pi,\n",
+    "        latent.mu,\n",
+    "        latent.z,\n",
+    "        latent.pi_sub,\n",
+    "        mu_sub_new,\n",
+    "        latent.z_sub\n",
+    "    )\n",
+    "\n",
+    "def gibbs_z_sub(key, data_nL, latent: Latent):\n",
+    "    log_pi_sub = jnp.log(latent.pi_sub)\n",
+    "    def log_pdf(x, z, mus, log_pis):\n",
+    "        mu = mus[z]\n",
+    "        log_pi = log_pis[z]\n",
+    "        z_scores = (x - mu) / (latent.sigma)\n",
+    "        log_p = log_pi + jnp.sum(jax.scipy.stats.norm.logpdf(z_scores))\n",
+    "        return log_p\n",
+    "\n",
+    "\n",
+    "    sub_func = jax.vmap(log_pdf, in_axes=(None, None, 0, 1))\n",
+    "\n",
+    "    log_probs = jax.vmap(sub_func, in_axes=(0, 0, None, None))(data_nL, latent.z, latent.mu_sub, log_pi_sub)\n",
+    "    z_sub_new = jax.random.categorical(key, log_probs)\n",
+    "\n",
+    "    return Latent(\n",
+    "        alpha=latent.alpha, \n",
+    "        sigma=latent.sigma, \n",
+    "        K=latent.K,\n",
+    "        pi=latent.pi,\n",
+    "        mu=latent.mu,\n",
+    "        z=latent.z,\n",
+    "        pi_sub=latent.pi_sub,\n",
+    "        mu_sub=latent.mu_sub,\n",
+    "        z_sub=z_sub_new,\n",
+    "        )\n",
+    "\n",
+    "@partial(jax.jit, static_argnames=('K',))\n",
+    "def gibbs_sweep(key, data_nL, latent: Latent, K: int):\n",
+    "    key, *subkeys = jax.random.split(key, 7)\n",
+    "    latent = gibbs_pi(subkeys[0], data_nL, latent, K)\n",
+    "    latent = gibbs_pi_sub(subkeys[3], data_nL, latent)\n",
+    "    latent = gibbs_mu(subkeys[1], data_nL, latent)\n",
+    "    latent = gibbs_mu_sub(subkeys[4], data_nL, latent)\n",
+    "    latent = gibbs_z(subkeys[2], data_nL, latent)\n",
+    "    latent = gibbs_z_sub(subkeys[5], data_nL, latent)\n",
+    "    return latent\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "N = 50\n",
+    "D = 2\n",
+    "K = 4\n",
+    "K_max = 7\n",
+    "alpha = 2.0\n",
+    "sigma_obs = 1.00\n",
+    "\n",
+    "key = jax.random.key(0)\n",
+    "key, subkey = jax.random.split(key)\n",
+    "mu_true = 2*jax.random.normal(subkey, (K, D))\n",
+    "z_true = jax.random.categorical(subkey, jnp.ones(K), shape=(N,))\n",
+    "data = jax.random.normal(key, (N, D)) * sigma_obs + mu_true[z_true]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Define colors for clusters\n",
+    "\n",
+    "# Plot the data points with inferred clusters\n",
+    "for k in range(K):\n",
+    "    plt.scatter(data[z_true == k, 0], data[z_true == k, 1], label=f'Cluster {k}')\n",
+    "\n",
+    "# Plot the inferred means\n",
+    "plt.scatter(mu_true[:K, 0], mu_true[:K, 1], color='red', marker='x', s=100, label='Cluster Means')\n",
+    "\n",
+    "plt.xlabel('Dimension 1')\n",
+    "plt.ylabel('Dimension 2')\n",
+    "plt.legend()\n",
+    "plt.title('Data Points and Cluster Means')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "latent = init_latent(key, alpha, sigma_obs, N, D, K-1, K_max)\n",
+    "latent_history = [latent]\n",
+    "for i in range(10):\n",
+    "    key, subkey = jax.random.split(key)\n",
+    "    latent = gibbs_sweep(subkey, data, latent, K-1)\n",
+    "    latent_history.append(latent)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Define colors for clusters\n",
+    "\n",
+    "# Plot the data points with inferred clusters\n",
+    "for k in range(K-1):\n",
+    "    plt.scatter(data[latent.z == k, 0], data[latent.z == k, 1], label=f'Inferred Cluster {k}')\n",
+    "\n",
+    "# Plot the inferred means\n",
+    "plt.scatter(latent.mu[:K-1, 0], latent.mu[:K-1, 1], color='red', marker='x', s=100, label='Inferred Means')\n",
+    "\n",
+    "plt.xlabel('Dimension 1')\n",
+    "plt.ylabel('Dimension 2')\n",
+    "plt.legend()\n",
+    "plt.title('Data Points and Inferred Cluster Means')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.animation as animation\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "def update(frame):\n",
+    "    ax.clear()\n",
+    "    latent = latent_history[frame]\n",
+    "    for k in range(K-1):\n",
+    "    # for k in range(K):\n",
+    "        ax.scatter(data[latent.z == k, 0], data[latent.z == k, 1], label=f'Inferred Cluster {k}')\n",
+    "    # ax.scatter(latent.mu[:K, 0], latent.mu[:K, 1], color='red', marker='x', s=100, label='Inferred Means')\n",
+    "    ax.scatter(latent.mu[:K-1, 0], latent.mu[:K-1, 1], color='red', marker='x', s=100, label='Inferred Means')\n",
+    "    ax.set_xlabel('Dimension 1')\n",
+    "    ax.set_ylabel('Dimension 2')\n",
+    "    ax.legend()\n",
+    "    ax.set_title(f'Iteration {frame}')\n",
+    "\n",
+    "ani = animation.FuncAnimation(fig, update, frames=len(latent_history), repeat=False)\n",
+    "ani.save('inferred_clusters_animation.mp4', writer='ffmpeg')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.patches as patches\n",
+    "\n",
+    "# Define markers and colors\n",
+    "markers = ['o', 's', 'D', '^', 'v', '<', '>']\n",
+    "colors = ['blue', 'green', 'orange', 'purple', 'brown', 'pink', 'gray']\n",
+    "# Add index labels near the means\n",
+    "for k in range(K-1):\n",
+    "    plt.text(latent.mu[k, 0], latent.mu[k, 1], f'{k}', fontsize=12, color='red', ha='right')\n",
+    "    for j in range(2):\n",
+    "        idx = (latent.z == k) & (latent.z_sub == j)\n",
+    "        plt.scatter(data[idx, 0], data[idx, 1], label=f'Inferred Cluster {k}-{j}', marker=markers[k % len(markers)])\n",
+    "\n",
+    "    # Plot the inferred means\n",
+    "    plt.scatter(latent.mu[k, 0], latent.mu[k, 1], color=colors[k], marker='x', s=100, label=f'Inferred Mean {k}')\n",
+    "    \n",
+    "    # Plot the circle indicating one standard deviation away\n",
+    "    circle = patches.Circle((latent.mu[k, 0], latent.mu[k, 1]), radius=latent.sigma, fill=False, edgecolor='red', linestyle='--')\n",
+    "    plt.gca().add_patch(circle)\n",
+    "\n",
+    "    # Plot the inferred means for subclusters\n",
+    "    for j in range(2):\n",
+    "        plt.scatter(latent.mu_sub[j, k, 0], latent.mu_sub[j, k, 1], color=colors[k], marker='+', s=100, label=f'Inferred Subcluster Mean {k}-{j}')\n",
+    "\n",
+    "\n",
+    "plt.xlabel('Dimension 1')\n",
+    "plt.ylabel('Dimension 2')\n",
+    "# plt.legend()\n",
+    "plt.title('Data and Inferred Clusters with One Standard Deviation Circles')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def marginal_loglikelihood_sufficient_statistics(\n",
+    "        x_mean:jax.Array, \n",
+    "        x_sum_sq:jax.Array, \n",
+    "        N:int, \n",
+    "        sigma_sq: float, \n",
+    "        mu_0: float, \n",
+    "        sigma_sq_0: float\n",
+    "    ):\n",
+    "    c = 0.5*jnp.log(sigma_sq) - 0.5 * (N*jnp.log(2 * jnp.pi * sigma_sq) + jnp.log(N*sigma_sq_0 + sigma_sq))\n",
+    "\n",
+    "    A = -x_sum_sq / (2 * sigma_sq) - mu_0**2 / 2 / sigma_sq_0\n",
+    "\n",
+    "    denom = 2*(N * sigma_sq_0 + sigma_sq)\n",
+    "    numer = sigma_sq_0 * N**2 * x_mean**2 / sigma_sq + sigma_sq * mu_0**2 / sigma_sq_0 + 2 * N * x_mean * mu_0\n",
+    "    B = denom / numer\n",
+    "    return c + A + B\n",
+    "\n",
+    "def marginal_loglikelihood(x, sigma_sq, mu_0, sigma_sq_0):\n",
+    "    x_mean = jnp.mean(x, axis=0)\n",
+    "    x_sum_sq = jnp.sum(x**2, axis=0)\n",
+    "\n",
+    "    N = x.shape[0]\n",
+    "    return marginal_loglikelihood_sufficient_statistics(x_mean, x_sum_sq, N, sigma_sq, mu_0, sigma_sq_0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def mh_split(key, data, latent):\n",
+    "    K_max = latent.pi.shape[0]-1\n",
+    "    idx = latent.z + latent.z_sub * K_max\n",
+    "\n",
+    "    sub_counts = jax.ops.segment_sum(jnp.ones(latent.z.shape), idx, 2*K_max)\n",
+    "    counts = jnp.bincount(latent.z, length=K_max)\n",
+    "\n",
+    "    x_sum_sub = jax.ops.segment_sum(data, idx, 2*K_max)\n",
+    "    x_mean_sub = x_sum_sub / sub_counts.reshape(-1, 1)\n",
+    "    x_sum_sq_sub = jax.ops.segment_sum(data**2, idx, 2*K_max)\n",
+    "\n",
+    "    x_sum = jax.ops.segment_sum(data, latent.z, K_max)\n",
+    "    x_sum_sq = jax.ops.segment_sum(data**2, latent.z, K_max)\n",
+    "    x_mean = x_sum / counts.reshape(-1, 1)\n",
+    "\n",
+    "    log_p_sub = jax.vmap(\n",
+    "        marginal_loglikelihood_sufficient_statistics, \n",
+    "        in_axes=(0, 0, 0, None, None, None))(x_mean_sub, x_sum_sq_sub, sub_counts, latent.sigma, 0.0, 1.0)\n",
+    "        \n",
+    "    log_p_sub = jnp.where(jnp.isnan(log_p_sub), 0.0, log_p_sub)\n",
+    "    log_p_sub = jnp.sum(log_p_sub, axis=1)\n",
+    "    log_p_sub = jnp.sum(log_p_sub.reshape(2, K_max), axis=0)\n",
+    "\n",
+    "    log_p = jax.vmap(\n",
+    "        marginal_loglikelihood_sufficient_statistics, \n",
+    "        in_axes=(0, 0, 0, None, None, None))(x_mean, x_sum_sq, counts, latent.sigma, 0.0, 1.0)\n",
+    "    log_p = jnp.where(jnp.isnan(log_p), 0.0, log_p)\n",
+    "    log_p = jnp.sum(log_p, axis=1)\n",
+    "\n",
+    "    sub_counts = sub_counts.reshape(2, K_max).T\n",
+    "    ratio = jnp.log(latent.alpha) + jnp.sum(jax.scipy.special.gammaln(sub_counts), axis=1) - jax.scipy.special.gammaln(counts)\n",
+    "    ratio = ratio + log_p_sub\n",
+    "    ratio = ratio - log_p\n",
+    "    ratio = jnp.where(jnp.isnan(ratio), -jnp.inf, ratio)\n",
+    "    ratio = jnp.where(ratio > 0, 0, ratio)\n",
+    "    ratio = jnp.where(ratio == jnp.inf, 0, ratio)\n",
+    "    ratio = jnp.exp(ratio)\n",
+    "    accepts = jax.random.uniform(key, (ratio.shape[0],)) < ratio\n",
+    "    return accepts\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0 1 2]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Latent(alpha=Array(2., dtype=float32, weak_type=True), sigma=Array(1., dtype=float32, weak_type=True), K=Array(3, dtype=int32, weak_type=True), pi=Array([0.18095681, 0.37822875, 0.41257277, 0.0282417 , 0.        ,\n",
+       "       0.        , 0.        , 0.        ], dtype=float32), mu=Array([[ 0.48372495, -1.3636018 ],\n",
+       "       [-1.2735299 , -0.40752804],\n",
+       "       [ 0.4500947 ,  2.032709  ],\n",
+       "       [ 1.6798486 , -0.25979078],\n",
+       "       [ 0.96907634, -0.7511777 ],\n",
+       "       [-0.5653657 , -0.5354302 ],\n",
+       "       [-0.9029019 ,  0.92294025]], dtype=float32), z=Array([2, 1, 0, 0, 1, 2, 0, 1, 1, 0, 1, 1, 2, 2, 1, 1, 0, 2, 1, 2, 0, 2,\n",
+       "       2, 2, 2, 2, 2, 1, 2, 1, 1, 2, 0, 1, 2, 1, 2, 0, 1, 2, 2, 0, 2, 1,\n",
+       "       1, 2, 2, 2, 0, 0], dtype=int32), pi_sub=Array([[0.8971081 , 0.1028919 ],\n",
+       "       [0.796864  , 0.203136  ],\n",
+       "       [0.5158339 , 0.48416606],\n",
+       "       [0.8963228 , 0.10367718],\n",
+       "       [0.7453937 , 0.25460625],\n",
+       "       [0.1640812 , 0.8359188 ],\n",
+       "       [0.9295871 , 0.07041288]], dtype=float32), mu_sub=Array([[[ 0.87291455, -0.673888  ],\n",
+       "        [-0.7061761 , -1.2426327 ],\n",
+       "        [-0.511562  ,  2.012715  ],\n",
+       "        [ 0.4797941 ,  1.4037753 ],\n",
+       "        [ 0.61419064, -0.07170032],\n",
+       "        [-1.1396977 , -2.082589  ],\n",
+       "        [-0.8026148 ,  0.24177451]],\n",
+       "\n",
+       "       [[-1.013991  ,  0.13363044],\n",
+       "        [-3.0499272 ,  0.30812076],\n",
+       "        [ 1.3946197 ,  1.7848582 ],\n",
+       "        [-1.3476132 ,  0.19613343],\n",
+       "        [-0.6585017 ,  1.8039787 ],\n",
+       "        [ 0.52953553, -0.08053942],\n",
+       "        [ 1.1491245 , -0.4790153 ]]], dtype=float32), z_sub=Array([1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0,\n",
+       "       0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1,\n",
+       "       0, 0, 1, 0, 0, 0], dtype=int32))"
+      ]
+     },
+     "execution_count": 207,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def normalize_latent(latent, K):\n",
+    "    z = latent.z\n",
+    "    unique_vals = jnp.unique(z, size=K, fill_value=1000000000)\n",
+    "    # print(unique_vals)\n",
+    "\n",
+    "    # latent.mu[]\n",
+    "    z_new = jnp.searchsorted(unique_vals, z, method='sort')\n",
+    "\n",
+    "    pi = latent.pi\n",
+    "    return Latent(\n",
+    "        latent.alpha,\n",
+    "        latent.sigma,\n",
+    "        latent.K,\n",
+    "        latent.pi,\n",
+    "        latent.mu,\n",
+    "        z_new,\n",
+    "        latent.pi_sub,\n",
+    "        latent.mu_sub,\n",
+    "        latent.z_sub\n",
+    "    )\n",
+    "\n",
+    "normalize_latent(latent, 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 209,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Array([1, 2, 3, 5], dtype=int32)"
+      ]
+     },
+     "execution_count": 209,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "jnp.unique(jnp.array([5,3,1,2]), fill_value=100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/mkdocs.yml b/mkdocs.yml
index b1e48e3..96b32b0 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -23,7 +23,9 @@ plugins:
 nav: 
   - Home: index.md
   - Tutorials: 
-    - Getting Started: tutorials/test.py
+    - Getting Started: tutorials/getting_started.py
+    - DPMMs from the Ground Up: tutorials/dpmm_ground_up.ipynb
+    - Splitting Clusters: tutorials/sub_cluster.ipynb
   - Library Reference: 
     - API: reference/api.md
     - Conjugacy: reference/conjugacy.md
diff --git a/notebooks/scratch.ipynb b/notebooks/scratch.ipynb
deleted file mode 100644
index 9dadc03..0000000
--- a/notebooks/scratch.ipynb
+++ /dev/null
@@ -1,452 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import jax\n",
-    "import jax.numpy as jnp\n",
-    "import matplotlib.pyplot as plt\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 140,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "key = jax.random.key(0)\n",
-    "\n",
-    "K_true = 4\n",
-    "D = 3\n",
-    "N = 1000\n",
-    "key, subkey = jax.random.split(key)\n",
-    "mu = jax.random.normal(subkey, (K_true,D))\n",
-    "\n",
-    "key, subkey = jax.random.split(key)\n",
-    "pi_true = jax.random.dirichlet(subkey, jnp.ones(K_true))\n",
-    "y_labels = jax.random.categorical(subkey, pi_true, shape=(N,))\n",
-    "key, subkey = jax.random.split(key)\n",
-    "sigma_hyper = 1.0\n",
-    "data = jax.random.normal(subkey, (N, D)) * sigma_hyper + mu[y_labels]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 141,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.scatter(data[:, 0], data[:, 1], c=y_labels, cmap='viridis')\n",
-    "plt.scatter(mu[:, 0], mu[:, 1], c='red', marker='x', s=100, label='True means')\n",
-    "plt.legend()\n",
-    "plt.xlabel('Feature 1')\n",
-    "plt.ylabel('Feature 2')\n",
-    "plt.title('Scatter plot of data with colors from y_labels')\n",
-    "plt.colorbar(label='Cluster')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 142,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from dataclasses import dataclass\n",
-    "from functools import partial\n",
-    "\n",
-    "@partial(jax.tree_util.register_dataclass,\n",
-    "         data_fields=('alpha', 'assignments', 'mu', 'pi'),\n",
-    "         meta_fields = ())\n",
-    "@dataclass\n",
-    "class Latent:\n",
-    "    alpha: float\n",
-    "    assignments: jax.Array\n",
-    "    mu: jax.Array\n",
-    "    pi: jax.Array\n",
-    "\n",
-    "def gibbs_pi(key, data_NL, latent: Latent):\n",
-    "    counts = jax.numpy.bincount(latent.assignments, length=latent.pi.shape[0])\n",
-    "    pi_new = jax.random.dirichlet(key, counts + latent.alpha)\n",
-    "    return Latent(alpha=latent.alpha, assignments=latent.assignments, mu=latent.mu, pi=pi_new)\n",
-    "\n",
-    "def gibbs_mu(key, data_nL, latent: Latent, i: int):\n",
-    "    N = jnp.count_nonzero(latent.assignments == i)\n",
-    "    sigma_sq_posterior = 1/(1 + N / sigma_hyper**2)\n",
-    "    x_sum = jnp.sum(data_nL[latent.assignments == i], axis=0)\n",
-    "    mu_posterior = sigma_sq_posterior / sigma_hyper**2 * x_sum\n",
-    "    mu_new = jax.random.normal(key, (D,)) * jnp.sqrt(sigma_sq_posterior) + mu_posterior\n",
-    "    return Latent(alpha=latent.alpha, assignments=latent.assignments, mu=latent.mu.at[i].set(mu_new), pi=latent.pi)\n",
-    "\n",
-    "def gibbs_assignments(key, data_nL, latent: Latent, i:int):\n",
-    "    key, subkey = jax.random.split(key)\n",
-    "    pi = latent.pi\n",
-    "    mu = latent.mu\n",
-    "    z_scores = (data_nL[i] - mu) / sigma_hyper\n",
-    "    logp = jax.scipy.stats.norm.logpdf(z_scores)\n",
-    "    logp = jnp.sum(logp, axis=1)\n",
-    "    log_pi = jnp.log(pi)\n",
-    "    logp = logp + log_pi\n",
-    "    key, subkey = jax.random.split(key)\n",
-    "    assignment = jax.random.categorical(subkey, logp)\n",
-    "    new_assignments = latent.assignments.at[i].set(assignment)  \n",
-    "    return Latent(alpha=latent.alpha, assignments=new_assignments, mu=latent.mu, pi=latent.pi)\n",
-    "\n",
-    "def init(key, N, K_max, D):\n",
-    "    key, subkey = jax.random.split(key)\n",
-    "    mu = jax.random.normal(subkey, (K_max,D))\n",
-    "    key, subkey = jax.random.split(key)\n",
-    "    assignments = jax.random.randint(subkey, (N,), 0, K_max)\n",
-    "    pi = jax.random.dirichlet(key, jax.numpy.ones(K_max))\n",
-    "    return Latent(alpha=1.0, assignments=assignments, mu=mu, pi=pi)\n",
-    "\n",
-    "\n",
-    "key = jax.random.key(0)\n",
-    "latent = init(key, N, K_true, D)\n",
-    "\n",
-    "gibbs_pi_jit = jax.jit(gibbs_pi)\n",
-    "gibbs_assignments_jit = gibbs_assignments\n",
-    "gibbs_mu_jit = gibbs_mu\n",
-    "\n",
-    "latent = gibbs_pi_jit(subkey, data, latent)\n",
-    "latent = gibbs_assignments_jit(key, data, latent, 0)\n",
-    "latent = gibbs_mu(subkey, data, latent, 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def gibbs_sweep(key, data, latent):\n",
-    "    key, subkey = jax.random.split(key)\n",
-    "    latent = gibbs_pi_jit(subkey, data, latent)\n",
-    "    for i in range(len(data)):\n",
-    "        key, subkey = jax.random.split(key)\n",
-    "        latent = gibbs_assignments_jit(subkey, data, latent, i)\n",
-    "\n",
-    "    for i in range(latent.mu.shape[0]):\n",
-    "        key, subkey = jax.random.split(key)\n",
-    "        latent = gibbs_mu_jit(subkey, data, latent, i)\n",
-    "    return latent\n",
-    "\n",
-    "latent = init(key, N, K_true, D)\n",
-    "latent_history = [latent]\n",
-    "for i in range(20):\n",
-    "    key, subkey = jax.random.split(key)\n",
-    "    latent = gibbs_sweep(key, data, latent)\n",
-    "    latent_history.append(latent)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.scatter(data[:, 0], data[:, 1], c=latent.assignments, cmap='viridis', alpha=1.0, label='Data points')\n",
-    "plt.scatter(latent.mu[:, 0], latent.mu[:, 1], c='red', marker='x', s=100, label='Inferred means')\n",
-    "plt.xlabel('Feature 1')\n",
-    "plt.ylabel('Feature 2')\n",
-    "plt.title('Scatter plot of data with inferred latents')\n",
-    "plt.colorbar(label='Cluster')\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.animation as animation\n",
-    "\n",
-    "fig, ax = plt.subplots()\n",
-    "\n",
-    "def animate(i):\n",
-    "    ax.clear()\n",
-    "    latent = latent_history[i]\n",
-    "    ax.scatter(data[:, 0], data[:, 1], c=latent.assignments, cmap='viridis', alpha=0.5, label='Data points')\n",
-    "    ax.scatter(latent.mu[:, 0], latent.mu[:, 1], c='red', marker='x', s=100, label='Inferred means')\n",
-    "    ax.set_xlabel('Feature 1')\n",
-    "    ax.set_ylabel('Feature 2')\n",
-    "    ax.set_title(f'Scatter plot of data with inferred latents (Iteration {i+1})')\n",
-    "    ax.legend()\n",
-    "\n",
-    "ani = animation.FuncAnimation(fig, animate, frames=len(latent_history), interval=200)\n",
-    "\n",
-    "ani.save('latent_evolution.mp4', writer='ffmpeg')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## K-Cluster Vectorized Inference"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 143,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Latent(alpha=Array(1., dtype=float32, weak_type=True), assignments=Array([1, 1, 0, 0, 3, 1, 0, 0, 0, 1, 1, 2, 3, 0, 1, 3, 1, 0, 2, 2, 0, 0,\n",
-       "       3, 3, 1, 2, 1, 0, 0, 0, 0, 2, 1, 3, 3, 3, 0, 3, 3, 1, 1, 0, 1, 3,\n",
-       "       1, 0, 1, 2, 3, 1, 0, 1, 1, 1, 0, 3, 2, 2, 2, 1, 2, 1, 1, 1, 3, 3,\n",
-       "       2, 1, 1, 0, 2, 1, 1, 1, 0, 1, 0, 1, 1, 3, 3, 1, 3, 3, 1, 2, 2, 0,\n",
-       "       1, 1, 3, 1, 0, 3, 3, 2, 2, 3, 2, 0, 1, 0, 2, 1, 2, 1, 1, 2, 1, 1,\n",
-       "       3, 2, 1, 3, 0, 3, 0, 1, 1, 1, 3, 0, 2, 1, 1, 0, 0, 1, 0, 2, 1, 1,\n",
-       "       1, 0, 1, 3, 0, 1, 2, 2, 2, 2, 0, 1, 2, 0, 1, 3, 1, 3, 0, 1, 3, 1,\n",
-       "       3, 1, 1, 3, 3, 3, 1, 2, 0, 1, 1, 3, 2, 1, 2, 1, 1, 0, 3, 2, 3, 2,\n",
-       "       1, 2, 2, 0, 1, 0, 1, 0, 3, 1, 1, 3, 3, 1, 0, 3, 1, 3, 2, 1, 1, 1,\n",
-       "       3, 0, 2, 2, 2, 2, 3, 3, 1, 1, 3, 2, 2, 1, 2, 0, 1, 2, 1, 3, 3, 2,\n",
-       "       3, 0, 1, 2, 2, 3, 3, 0, 3, 3, 2, 2, 2, 0, 0, 2, 1, 0, 3, 3, 2, 3,\n",
-       "       1, 3, 2, 1, 3, 1, 3, 2, 2, 2, 3, 3, 1, 1, 0, 1, 3, 1, 3, 0, 0, 2,\n",
-       "       3, 0, 2, 3, 0, 2, 3, 1, 2, 1, 2, 3, 3, 2, 3, 3, 2, 0, 1, 1, 1, 0,\n",
-       "       1, 0, 1, 2, 1, 0, 3, 3, 0, 3, 2, 2, 2, 1, 0, 3, 0, 3, 2, 3, 2, 1,\n",
-       "       1, 2, 3, 2, 0, 0, 1, 2, 0, 3, 0, 3, 0, 0, 0, 0, 1, 0, 0, 1, 1, 3,\n",
-       "       1, 2, 0, 1, 2, 3, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 0, 3, 3, 0, 1,\n",
-       "       0, 3, 3, 1, 1, 2, 0, 1, 3, 0, 3, 2, 3, 2, 3, 0, 0, 1, 2, 0, 3, 0,\n",
-       "       0, 1, 2, 2, 3, 0, 1, 1, 1, 1, 0, 3, 0, 3, 1, 1, 0, 2, 3, 1, 2, 3,\n",
-       "       1, 2, 3, 3, 2, 3, 2, 3, 3, 1, 2, 3, 1, 0, 0, 3, 3, 2, 0, 3, 3, 1,\n",
-       "       3, 3, 1, 3, 2, 3, 1, 3, 0, 1, 3, 2, 2, 1, 0, 1, 1, 3, 2, 3, 3, 1,\n",
-       "       0, 1, 1, 3, 3, 3, 0, 0, 0, 0, 3, 1, 2, 3, 0, 0, 1, 0, 1, 3, 2, 2,\n",
-       "       0, 0, 3, 1, 2, 2, 0, 1, 3, 3, 0, 3, 3, 2, 0, 2, 2, 3, 3, 1, 0, 1,\n",
-       "       0, 2, 3, 2, 0, 0, 2, 2, 1, 3, 1, 2, 0, 0, 1, 3, 1, 2, 3, 0, 0, 0,\n",
-       "       1, 0, 1, 1, 1, 1, 2, 1, 3, 2, 2, 2, 0, 1, 3, 1, 1, 1, 2, 3, 0, 1,\n",
-       "       3, 1, 3, 0, 1, 3, 1, 3, 0, 2, 1, 0, 1, 0, 2, 1, 2, 3, 2, 0, 0, 1,\n",
-       "       1, 0, 2, 0, 0, 2, 1, 2, 3, 3, 2, 0, 3, 0, 3, 2, 2, 1, 0, 3, 0, 2,\n",
-       "       0, 0, 2, 1, 0, 2, 2, 1, 3, 1, 3, 3, 2, 0, 0, 3, 1, 0, 1, 3, 2, 3,\n",
-       "       1, 2, 2, 1, 3, 2, 0, 2, 0, 2, 0, 1, 3, 2, 0, 3, 1, 1, 3, 1, 3, 0,\n",
-       "       1, 3, 0, 2, 1, 3, 1, 2, 1, 2, 3, 0, 3, 0, 0, 2, 0, 0, 2, 2, 1, 0,\n",
-       "       2, 3, 2, 1, 0, 0, 2, 1, 2, 3, 2, 3, 0, 2, 3, 0, 3, 0, 0, 1, 3, 0,\n",
-       "       3, 1, 1, 2, 0, 0, 0, 1, 3, 2, 2, 1, 3, 2, 1, 0, 0, 3, 0, 2, 3, 1,\n",
-       "       1, 3, 1, 1, 0, 2, 3, 1, 3, 0, 0, 2, 2, 1, 2, 2, 0, 2, 2, 3, 3, 3,\n",
-       "       3, 3, 1, 0, 2, 2, 3, 1, 3, 0, 0, 2, 0, 1, 2, 0, 0, 0, 0, 1, 2, 0,\n",
-       "       1, 0, 0, 2, 2, 0, 3, 2, 1, 1, 2, 0, 2, 2, 0, 2, 0, 3, 1, 1, 1, 0,\n",
-       "       3, 0, 2, 0, 3, 2, 0, 2, 2, 3, 0, 1, 1, 2, 1, 0, 3, 1, 2, 0, 0, 3,\n",
-       "       1, 0, 3, 3, 1, 1, 2, 1, 0, 1, 2, 2, 2, 3, 1, 1, 2, 2, 3, 2, 0, 0,\n",
-       "       2, 0, 2, 0, 2, 2, 2, 0, 0, 0, 3, 1, 3, 2, 0, 0, 2, 1, 2, 1, 3, 3,\n",
-       "       1, 1, 1, 3, 3, 2, 0, 1, 1, 3, 2, 2, 2, 2, 1, 1, 0, 1, 2, 3, 3, 0,\n",
-       "       2, 1, 3, 1, 2, 1, 0, 3, 1, 1, 3, 2, 0, 1, 1, 1, 2, 3, 2, 1, 3, 1,\n",
-       "       0, 2, 0, 1, 3, 1, 1, 1, 1, 2, 0, 0, 3, 1, 0, 1, 3, 0, 3, 1, 3, 1,\n",
-       "       0, 1, 3, 1, 3, 3, 0, 1, 1, 1, 3, 3, 3, 0, 2, 2, 3, 3, 3, 1, 0, 1,\n",
-       "       1, 0, 3, 1, 2, 1, 3, 3, 3, 3, 0, 3, 0, 0, 1, 3, 0, 1, 3, 1, 1, 1,\n",
-       "       1, 0, 0, 2, 0, 2, 2, 1, 2, 1, 3, 1, 2, 2, 0, 0, 1, 3, 0, 1, 2, 1,\n",
-       "       3, 0, 2, 2, 1, 1, 1, 1, 3, 0, 3, 3, 0, 3, 2, 1, 1, 1, 1, 1, 2, 3,\n",
-       "       1, 1, 3, 1, 2, 1, 0, 3, 1, 1, 2, 0, 2, 0, 1, 0, 2, 3, 0, 2, 0, 0,\n",
-       "       1, 3, 2, 3, 2, 1, 0, 3, 3, 3], dtype=int32), mu=Array([[ 0.02534657, -0.34630996,  0.70135725],\n",
-       "       [ 0.07830606, -0.38867092,  0.7189667 ],\n",
-       "       [ 0.24621397, -0.4798445 ,  0.9164252 ],\n",
-       "       [ 0.19147222, -0.5728084 ,  0.8341254 ]], dtype=float32), pi=Array([0.22434255, 0.28109145, 0.22385709, 0.2706808 ], dtype=float32))"
-      ]
-     },
-     "execution_count": 143,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def gibbs_pi(key, data_nL, latent: Latent):\n",
-    "    counts = jnp.bincount(latent.assignments, length=latent.pi.shape[0])\n",
-    "    concentrations = jnp.concat([counts, jnp.array([latent.alpha])], axis=0)\n",
-    "    pi_new = jax.random.dirichlet(key, concentrations)[:-1]\n",
-    "    return Latent(alpha=latent.alpha, assignments=latent.assignments, mu=latent.mu, pi=pi_new)\n",
-    "\n",
-    "def gibbs_mu(key, data_nL, latent: Latent):\n",
-    "    K = latent.mu.shape[0]\n",
-    "    counts = jnp.bincount(latent.assignments, length=K)\n",
-    "    sigma_sq_posterior = 1/(1 + counts / sigma_hyper**2)\n",
-    "    x_sum = jax.ops.segment_sum(data_nL, latent.assignments, K)\n",
-    "    mu_posterior = sigma_sq_posterior.reshape(-1,1) * x_sum / sigma_hyper**2 \n",
-    "    mu_new = jax.random.normal(key, (K,D)) * jnp.sqrt(sigma_sq_posterior).reshape(-1,1) + mu_posterior\n",
-    "    return Latent(alpha=latent.alpha, assignments=latent.assignments, mu=mu_new, pi=latent.pi)\n",
-    "\n",
-    "def gibbs_assignments(key, data_nL, latent: Latent):\n",
-    "    log_pi = jnp.log(latent.pi)\n",
-    "    def pdf(x, mu, log_pi):\n",
-    "        z_scores = (x - mu) / sigma_hyper\n",
-    "        log_p = log_pi + jnp.sum(jax.scipy.stats.norm.logpdf(z_scores))\n",
-    "        return log_p\n",
-    "\n",
-    "    log_probs = jax.vmap(jax.vmap(pdf, in_axes=(None, 0, 0)), in_axes=(0, None, None))(data_nL, latent.mu, log_pi)\n",
-    "    assignments_new = jax.random.categorical(key, log_probs)\n",
-    "    return Latent(alpha=latent.alpha, assignments=assignments_new, mu=latent.mu, pi=latent.pi)\n",
-    "\n",
-    "def gibbs_sweep(key, data, latent):\n",
-    "    key, subkey = jax.random.split(key)\n",
-    "    latent = gibbs_pi(subkey, data, latent)\n",
-    "    latent = gibbs_mu(subkey, data, latent)\n",
-    "    latent = gibbs_assignments(subkey, data, latent)\n",
-    "    return latent\n",
-    "\n",
-    "latent = init(key, N, K_true, D)\n",
-    "gibbs_sweep_jit = jax.jit(gibbs_sweep)\n",
-    "gibbs_sweep_jit(key, data, latent)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 144,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "key = jax.random.key(0)\n",
-    "latent = init(key, N, K_true, D)\n",
-    "latent_history = []\n",
-    "for i in range(100):\n",
-    "    key, subkey = jax.random.split(key)\n",
-    "    latent = gibbs_sweep_jit(subkey, data, latent)\n",
-    "    if i % 10 == 0:\n",
-    "        latent_history.append(latent)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 148,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Array([0.        , 0.3762794 , 0.25034556, 0.3731362 ], dtype=float32)"
-      ]
-     },
-     "execution_count": 148,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "latent.pi"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 145,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.scatter(data[:, 0], data[:, 1], c=latent.assignments, cmap='viridis', alpha=1.0, label='Data points')\n",
-    "plt.scatter(latent.mu[:, 0], latent.mu[:, 1], c='red', marker='x', s=100, label='Inferred means')\n",
-    "plt.xlabel('Feature 1')\n",
-    "plt.ylabel('Feature 2')\n",
-    "plt.title('Scatter plot of data with inferred latents')\n",
-    "plt.colorbar(label='Cluster')\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 146,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.animation as animation\n",
-    "\n",
-    "fig, ax = plt.subplots()\n",
-    "\n",
-    "def animate(i):\n",
-    "    ax.clear()\n",
-    "    latent = latent_history[i]\n",
-    "    ax.scatter(data[:, 0], data[:, 1], c=latent.assignments, cmap='viridis', alpha=0.5, label='Data points')\n",
-    "    ax.scatter(latent.mu[:, 0], latent.mu[:, 1], c='red', marker='x', s=100, label='Inferred means')\n",
-    "    ax.set_xlabel('Feature 1')\n",
-    "    ax.set_ylabel('Feature 2')\n",
-    "    ax.set_title(f'Scatter plot of data with inferred latents (Iteration {i+1})')\n",
-    "    ax.legend()\n",
-    "\n",
-    "ani = animation.FuncAnimation(fig, animate, frames=len(latent_history), interval=200)\n",
-    "\n",
-    "ani.save('latent_evolution.mp4', writer='ffmpeg')\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}