code: def __init__(self, obs_space, action_space, config):
      model = ModelCatalog.get_model_v2(
          obs_space,
          action_space,
          action_space.n,
          config["model"],
          "torch",
      )
      _, env_creator = Algorithm._get_env_id_and_creator(
          config["env"], config
      )
      if config["ranked_rewards"]["enable"]:
          # if r2 is enabled, tne env is wrapped to include a rewards buffer
          # used to normalize rewards
          env_cls = get_r2_env_wrapper(
              env_creator, config["ranked_rewards"]
          )
  
          # the wrapped env is used only in the mcts, not in the
          # rollout workers
          def _env_creator():
              return env_cls(config["env_config"])
  
      else:
  
          def _env_creator():
              return env_creator(config["env_config"])
  
      def mcts_creator():
          return MCTS(model, config["mcts_config"])
  
      super().__init__(
          obs_space,
          action_space,
          config,
          model,
          alpha_zero_loss,
          TorchCategorical,
          mcts_creator,
          _env_creator,
      )
  
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 AlphaZeroPolicyWrapperClass object from inherited base classes: AlphaZeroPolicy with example: obj = AlphaZeroPolicyWrapperClass(obs_space, action_space, config)