class_docstr: Abstract base class for RLlib callbacks (similar to Keras callbacks).
  
  These callbacks can be used for custom metrics and custom postprocessing.
  
  By default, all of these callbacks are no-ops. To configure custom training
  callbacks, subclass DefaultCallbacks and then set
  {"callbacks": YourCallbacksClass} in the algo config.
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docstr: Runs when the evaluation is done.
  
  Runs at the end of Algorithm.evaluate().
  
  Args:
      algorithm: Reference to the algorithm instance.
      evaluation_metrics: Results dict to be returned from algorithm.evaluate().
          You can mutate this object to add additional metrics.
      kwargs: Forward compatibility placeholder.
docstr_tok: ["Runs","when","the","evaluation","is","done",".","Runs","at","the","end","of","Algorithm.evaluate","(",")",".","Args",":","algorithm",":","Reference","to","the","algorithm","instance",".","evaluation_metrics",":","Results","dict","to","be","returned","from","algorithm.evaluate","(",")",".","You","can","mutate","this","object","to","add","additional","metrics",".","kwargs",":","Forward","compatibility","placeholder","."]
code: def on_evaluate_end(
      self,
      *,
      algorithm: "Algorithm",
      evaluation_metrics: dict,
      **kwargs,
  ) -> None:
      """Runs when the evaluation is done.
  
      Runs at the end of Algorithm.evaluate().
  
      Args:
          algorithm: Reference to the algorithm instance.
          evaluation_metrics: Results dict to be returned from algorithm.evaluate().
              You can mutate this object to add additional metrics.
          kwargs: Forward compatibility placeholder.
      """
      pass
  
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 13 for calling the DefaultCallbacks obj's underlying member method code with example usage: obj.on_evaluate_end() without return types