class_docstr: LeelaChessZero callbacks.
  If you use custom callbacks, you must extend this class and call super()
  for on_episode_start.
class_docstr_tok: ["LeelaChessZero","callbacks",".","If","you","use","custom","callbacks",",","you","must","extend","this","class","and","call","super","(",")","for","on_episode_start","."]
code: def __init__(self):
      super().__init__()
  
imports: import logging
  from typing import List, Optional, Type, Union
  from rllib_leela_chess_zero.leela_chess_zero.leela_chess_zero_model import LeelaChessZeroModel
  from rllib_leela_chess_zero.leela_chess_zero.leela_chess_zero_policy import LeelaChessZeroPolicy
  from rllib_leela_chess_zero.leela_chess_zero.mcts import MCTS
  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 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, SYNCH_WORKER_WEIGHTS_TIMER
  from ray.rllib.utils.replay_buffers import PrioritizedReplayBuffer
  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 LeelaChessZeroDefaultCallbacks object from inherited base classes: DefaultCallbacks with example: obj = LeelaChessZeroDefaultCallbacks()