diff --git a/chex/_src/variants_pytest_example.py b/chex/_src/variants_pytest_example.py
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+# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Example of using `chex.variants` with `pytest`."""
+
+from typing import Callable
+import chex
+from chex._src import variants
+import jax.numpy as jnp
+import pytest
+
+# `chex.variants` is primarily designed for `unittest.TestCase` and `absl.testing`.
+# When using `pytest`, you can manually parametrize your tests over
+# `variants.ALL_VARIANTS` (or a subset thereof) to achieve similar coverage.
+#
+# Note: `chex.variants` manages different JAX execution modes (JIT, PMAP, etc.)
+# by decorating the function-under-test.
+
+
+@pytest.mark.parametrize("variant", variants.ALL_VARIANTS)
+@pytest.mark.parametrize("n", [1, 2, 3])
+def test_variants_with_pytest(variant: Callable, n: int) -> None:
+  """Tests a function across all Chex variants using pytest parametrization.
+
+  Args:
+    variant: A Chex variant decorator (e.g., with_jit, without_jit, etc.).
+    n: Input parameter for the test.
+  """
+
+  # Define the computation you want to test.
+  # The `@variant` decorator will apply the specific execution mode
+  # (e.g., wrap in jax.jit, jax.pmap) for this test iteration.
+  @variant
+  def computation(x):
+    return x * x
+
+  # Execute the decorated function.
+  # Convert input to JAX array as some variants (like pmap) might expect it
+  # or handle it differently.
+  result = computation(jnp.array(n))
+
+  # Verify the result.
+  assert result == n * n