class_docstr: Wrapper around it's environment, to simplify configuration.
class_docstr_tok: ["Wrapper","around","it","'s","environment",",","to","simplify","configuration","."]
code: def max_steps(self) -> Optional[int]:
      possible_keys = [
          # gym should have _max_episode_steps
          "_max_episode_steps",
          # Minigrid should have max_steps
          "max_steps",
      ]
      for key in possible_keys:
          res = getattr(self.env, key, None)
          if res is not None:
              return res
      return None
  
imports: import abc
  import logging
  from typing import Callable, Optional
  import gym
  import numpy
  import reagent.core.types
  import torch
  from gym import spaces
  from reagent.core.dataclasses import dataclass
  from reagent.core.parameters import CONTINUOUS_TRAINING_ACTION_RANGE
  from reagent.core.registry_meta import RegistryMeta
  from reagent.training.utils import rescale_actions
label_desc: Use image node_id 11 for calling the EnvWrapper obj's underlying member method code with example usage: obj.max_steps() and returns: None, res