chore: import upstream snapshot with attribution

doc/source/tune/examples/includes/async_hyperband_example.rst

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+:orphan:
+
+Asynchronous HyperBand Example
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+This example demonstrates how to use Ray Tune's Asynchronous Successive Halving Algorithm (ASHA) scheduler 
+to efficiently optimize hyperparameters for a machine learning model. ASHA is particularly useful for 
+large-scale hyperparameter optimization as it can adaptively allocate resources and end 
+poorly performing trials early.
+
+Requirements: `pip install "ray[tune]"`
+
+.. literalinclude:: /../../python/ray/tune/examples/async_hyperband_example.py
+
+
+See Also
+--------
+
+- `ASHA Paper <https://arxiv.org/abs/1810.05934>`_

doc/source/tune/examples/includes/ax_example.rst

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+:orphan:
+
+AX Example
+~~~~~~~~~~
+
+.. literalinclude:: /../../python/ray/tune/examples/ax_example.py

doc/source/tune/examples/includes/bayesopt_example.rst

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+:orphan:
+
+BayesOpt Example
+~~~~~~~~~~~~~~~~
+
+.. literalinclude:: /../../python/ray/tune/examples/bayesopt_example.py

doc/source/tune/examples/includes/bohb_example.rst

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+:orphan:
+
+BOHB Example
+~~~~~~~~~~~~
+
+.. literalinclude:: /../../python/ray/tune/examples/bohb_example.py

doc/source/tune/examples/includes/custom_func_checkpointing.rst

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+:orphan:
+
+Custom Checkpointing Example
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. literalinclude:: /../../python/ray/tune/examples/custom_func_checkpointing.py

doc/source/tune/examples/includes/hyperband_example.rst

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+:orphan:
+
+HyperBand Example
+=================
+
+.. literalinclude:: /../../python/ray/tune/examples/hyperband_example.py

doc/source/tune/examples/includes/hyperband_function_example.rst

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+:orphan:
+
+HyperBand Function Example
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. literalinclude:: /../../python/ray/tune/examples/hyperband_function_example.py

doc/source/tune/examples/includes/hyperopt_conditional_search_space_example.rst

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+:orphan:
+
+Hyperopt Conditional Search Space Example
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. literalinclude:: /../../python/ray/tune/examples/hyperopt_conditional_search_space_example.py

doc/source/tune/examples/includes/logging_example.rst

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+:orphan:
+
+Logging Example
+~~~~~~~~~~~~~~~
+
+.. literalinclude:: /../../python/ray/tune/examples/logging_example.py

doc/source/tune/examples/includes/mlflow_ptl_example.rst

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+:orphan:
+
+MLflow PyTorch Lightning Example
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. literalinclude:: /../../python/ray/tune/examples/mlflow_ptl.py

doc/source/tune/examples/includes/mnist_ptl_mini.rst

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+:orphan:
+
+MNIST PyTorch Lightning Example
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. literalinclude:: /../../python/ray/tune/examples/mnist_ptl_mini.py

doc/source/tune/examples/includes/mnist_pytorch.rst

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+:orphan:
+
+MNIST PyTorch Example
+~~~~~~~~~~~~~~~~~~~~~
+
+.. literalinclude:: /../../python/ray/tune/examples/mnist_pytorch.py
+
+If you consider switching to PyTorch Lightning to get rid of some of your boilerplate
+training code, please know that we also have a walkthrough on :doc:`how to use Tune with
+PyTorch Lightning models </tune/examples/tune-pytorch-lightning>`.

doc/source/tune/examples/includes/mnist_pytorch_trainable.rst

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+:orphan:
+
+MNIST PyTorch Trainable Example
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. literalinclude:: /../../python/ray/tune/examples/mnist_pytorch_trainable.py

doc/source/tune/examples/includes/nevergrad_example.rst

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+:orphan:
+
+Nevergrad Example
+~~~~~~~~~~~~~~~~~
+
+.. literalinclude:: /../../python/ray/tune/examples/nevergrad_example.py

doc/source/tune/examples/includes/pb2_example.rst

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+:orphan:
+
+PB2 Example
+~~~~~~~~~~~
+
+.. literalinclude:: /../../python/ray/tune/examples/pb2_example.py

doc/source/tune/examples/includes/pb2_ppo_example.rst

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+:orphan:
+
+PB2 PPO Example
+~~~~~~~~~~~~~~~
+
+.. literalinclude:: /../../python/ray/tune/examples/pb2_ppo_example.py

doc/source/tune/examples/includes/pbt_convnet_function_example.rst

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+:orphan:
+
+PBT ConvNet Example
+~~~~~~~~~~~~~~~~~~~
+
+.. literalinclude:: /../../python/ray/tune/examples/pbt_convnet_function_example.py

doc/source/tune/examples/includes/pbt_example.rst

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+:orphan:
+
+PBT Example
+~~~~~~~~~~~
+
+.. literalinclude:: /../../python/ray/tune/examples/pbt_example.py

doc/source/tune/examples/includes/pbt_function.rst

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+:orphan:
+
+PBT Function Example
+~~~~~~~~~~~~~~~~~~~~
+
+The following script produces the following results. For a population of 8 trials,
+the PBT learning rate schedule roughly matches the optimal learning rate schedule.
+
+.. image:: images/pbt_function_results.png
+
+.. literalinclude:: /../../python/ray/tune/examples/pbt_function.py

doc/source/tune/examples/includes/pbt_memnn_example.rst

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+:orphan:
+
+Memory NN Example
+~~~~~~~~~~~~~~~~~
+
+.. literalinclude:: /../../python/ray/tune/examples/pbt_memnn_example.py

doc/source/tune/examples/includes/pbt_tune_cifar10_with_keras.rst

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+:orphan:
+
+Keras Cifar10 Example
+~~~~~~~~~~~~~~~~~~~~~
+
+.. literalinclude:: /../../python/ray/tune/examples/pbt_tune_cifar10_with_keras.py

doc/source/tune/examples/includes/tf_mnist_example.rst

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+:orphan:
+
+TensorFlow MNIST Example
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. literalinclude:: /../../python/ray/tune/examples/tf_mnist_example.py

doc/source/tune/examples/includes/tune_basic_example.rst

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+:orphan:
+
+tune_basic_example
+~~~~~~~~~~~~~~~~~~
+
+.. literalinclude:: /../../python/ray/tune/examples/tune_basic_example.py

doc/source/tune/examples/includes/xgboost_dynamic_resources_example.rst

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+:orphan:
+
+XGBoost Dynamic Resources Example
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. literalinclude:: /../../python/ray/tune/examples/xgboost_dynamic_resources_example.py