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: Callback run when a new environment object has been created.
  
  Note: This only applies to the new API stack. The env used is usually a
  gym.Env (or more specificallly a gym.vector.Env).
  
  Args:
      env_runner: Reference to the current EnvRunner instance.
      env: The environment object that has been created on `env_runner`. This is
          usually a gym.Env (or a gym.vector.Env) object.
      env_config: The config dict that has been passed to the `gym.make()` call
          as kwargs.
      kwargs: Forward compatibility placeholder.
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code: def on_environment_created(
      self,
      *,
      env_runner: "EnvRunner",
      env: gym.Env,
      env_config: EnvContext,
      **kwargs,
  ) -> None:
      """Callback run when a new environment object has been created.
  
      Note: This only applies to the new API stack. The env used is usually a
      gym.Env (or more specificallly a gym.vector.Env).
  
      Args:
          env_runner: Reference to the current EnvRunner instance.
          env: The environment object that has been created on `env_runner`. This is
              usually a gym.Env (or a gym.vector.Env) object.
          env_config: The config dict that has been passed to the `gym.make()` call
              as kwargs.
          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 6 for calling the DefaultCallbacks obj's underlying member method code with example usage: obj.on_environment_created() without return types