code: def get_default_policy_class(
      cls, config: AlgorithmConfig
  ) -> Optional[Type[Policy]]:
      return AlphaZeroPolicyWrapperClass
  
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 2 for calling the AlphaZero obj's underlying member method code with example usage: obj.get_default_policy_class(cls, config) and returns: AlphaZeroPolicyWrapperClass