class_docstr: Update input callbacks to mutate batch with states entropy rewards.
class_docstr_tok: ["Update","input","callbacks","to","mutate","batch","with","states","entropy","rewards","."]
code: def on_train_result(
      self, *, result: dict, algorithm=None, **kwargs
  ) -> None:
      # TODO(gjoliver): Remove explicit _step tracking and pass
      #  Algorithm._iteration as a parameter to on_learn_on_batch() call.
      RE3UpdateCallbacks._step = result["training_iteration"]
      super().on_train_result(
          algorithm=algorithm, result=result, **kwargs
      )
  
imports: import gc
  import os
  import platform
  import tracemalloc
  from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Type, Union
  import gymnasium
  import numpy
  from ray.rllib.core.rl_module.rl_module import RLModule
  from ray.rllib.env.base_env import BaseEnv
  from ray.rllib.env.env_context import EnvContext
  from ray.rllib.evaluation.episode import Episode
  from ray.rllib.evaluation.episode_v2 import EpisodeV2
  from ray.rllib.evaluation.postprocessing import Postprocessing
  from ray.rllib.policy import Policy
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.utils.annotations import override, OverrideToImplementCustomLogic, PublicAPI
  from ray.rllib.utils.deprecation import deprecation_warning
  from ray.rllib.utils.exploration.random_encoder import _MovingMeanStd, compute_states_entropy, update_beta
  from ray.rllib.utils.typing import AgentID, EnvType, EpisodeType, PolicyID
  from ray.tune.callback import _CallbackMeta
  import psutil
label_desc: Use image node_id 3 for calling the RE3UpdateCallbacks obj's underlying member method code with example usage: obj.on_train_result() without return types