class_docstr: A dummy conversion interface asserting that the action is packed into a dictionary space.
  
  :param env: Gym environment.
docstr: Converts environment MazeAction to agent action.
  
  :param maze_action: the environment MazeAction.
  :return: the agent action.
docstr_tok: ["Converts","environment","MazeAction","to","agent","action",".",":","param","maze_action",":","the","environment","MazeAction",".",":","return",":","the","agent","action","."]
code: def maze_to_space(
      self, maze_action: MazeActionType
  ) -> Dict[str, np.ndarray]:
      """Converts environment MazeAction to agent action.
  
      :param maze_action: the environment MazeAction.
      :return: the agent action.
      """
      if not self._original_space_is_dict:
          maze_action = {"action": maze_action}
      return maze_action
  
imports: from copy import copy, deepcopy
  from typing import Tuple, Union, Any, Dict, Optional
  import gym
  import numpy
  from gym.envs.classic_control import CartPoleEnv, MountainCarEnv, Continuous_MountainCarEnv, PendulumEnv, AcrobotEnv
  from gym.wrappers import TimeLimit
  from maze.core.annotations import override
  from maze.core.env.action_conversion import ActionConversionInterface
  from maze.core.env.core_env import CoreEnv
  from maze.core.env.maze_action import MazeActionType
  from maze.core.env.maze_env import MazeEnv
  from maze.core.env.maze_state import MazeStateType
  from maze.core.env.observation_conversion import ObservationConversionInterface
  from maze.core.env.simulated_env_mixin import SimulatedEnvMixin
  from maze.core.env.structured_env import StepKeyType, ActorID
  from maze.core.log_events.step_event_log import StepEventLog
  from maze.core.rendering.renderer import Renderer
label_desc: Use image node_id 3 for calling the GymActionConversion obj's underlying member method code with example usage: obj.maze_to_space(maze_action) and returns: maze_action