class_docstr: Defines a configuration class from which an AlphaZero Algorithm can be built.
  
  Example:
      >>> from rllib_alpha_zero.alpha_zero import AlphaZeroConfig
      >>> config = AlphaZeroConfig()   # doctest: +SKIP
      >>> config = config.training(sgd_minibatch_size=256)   # doctest: +SKIP
      >>> config = config..resources(num_gpus=0)   # doctest: +SKIP
      >>> config = config..rollouts(num_rollout_workers=4)   # doctest: +SKIP
      >>> print(config.to_dict()) # doctest: +SKIP
      >>> # Build a Algorithm object from the config and run 1 training iteration.
      >>> algo = config.build(env="CartPole-v1")  # doctest: +SKIP
      >>> algo.train() # doctest: +SKIP
  
  Example:
      >>> from rllib_alpha_zero.alpha_zero import AlphaZeroConfig
      >>> from ray import air
      >>> from ray import tune
      >>> config = AlphaZeroConfig()
      >>> # Print out some default values.
      >>> print(config.shuffle_sequences) # doctest: +SKIP
      >>> # Update the config object.
      >>> config.training(lr=tune.grid_search([0.001, 0.0001]))  # doctest: +SKIP
      >>> # Set the config object's env.
      >>> config.environment(env="CartPole-v1")   # doctest: +SKIP
      >>> # Use to_dict() to get the old-style python config dict
      >>> # when running with tune.
      >>> tune.Tuner( # doctest: +SKIP
      ...     "AlphaZero",
      ...     run_config=air.RunConfig(stop={"episode_reward_mean": 200}),
      ...     param_space=config.to_dict(),
      ... ).fit()
docstr: Initializes a PPOConfig instance.
imports: import logging
  from typing import List, Optional, Type, Union
  from rllib_alpha_zero.alpha_zero.alpha_zero_policy import AlphaZeroPolicy
  from rllib_alpha_zero.alpha_zero.mcts import MCTS
  from rllib_alpha_zero.alpha_zero.ranked_rewards import get_r2_env_wrapper
  from ray.rllib.algorithms.algorithm import Algorithm
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, NotProvided
  from ray.rllib.algorithms.callbacks import DefaultCallbacks
  from ray.rllib.execution.rollout_ops import synchronous_parallel_sample
  from ray.rllib.execution.train_ops import multi_gpu_train_one_step, train_one_step
  from ray.rllib.models.catalog import ModelCatalog
  from ray.rllib.models.modelv2 import restore_original_dimensions
  from ray.rllib.models.torch.torch_action_dist import TorchCategorical
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.sample_batch import concat_samples
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.deprecation import DEPRECATED_VALUE
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.metrics import NUM_AGENT_STEPS_SAMPLED, NUM_ENV_STEPS_SAMPLED, SAMPLE_TIMER, SYNCH_WORKER_WEIGHTS_TIMER
  from ray.rllib.utils.replay_buffers.utils import validate_buffer_config
  from ray.rllib.utils.typing import ResultDict
label_desc: Use image node_id 1 to create a new AlphaZeroConfig object from inherited base classes: AlgorithmConfig with example: obj = AlphaZeroConfig(algo_class)