class_docstr: Base wrapper implementing PyEnvironmentBaseWrapper interface for Gym envs.
  
  Action and observation specs are automatically generated from the action and
  observation spaces. See base class for py_environment.Base details.
class_docstr_tok: ["Base","wrapper","implementing","PyEnvironmentBaseWrapper","interface","for","Gym","envs",".","Action","and","observation","specs","are","automatically","generated","from","the","action","and","observation","spaces",".","See","base","class","for","py_environment.Base","details","."]
code: def action_spec(self) -> types.NestedArraySpec:
      return self._action_spec
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  from typing import Any, Dict, Optional, Text
  import gym
  import gym.spaces
  import numpy
  import tensorflow
  from tf_agents import specs
  from tf_agents.environments import py_environment
  from tf_agents.trajectories import time_step
  from tf_agents.typing import types
  from tensorflow.python.util import nest
label_desc: Use image node_id 10 for calling the GymWrapper obj's underlying member method code with example usage: obj.action_spec() and returns: self