class_docstr: A dummy conversion interface asserting that the action is packed into a dictionary space.
  
  :param env: Gym environment.
code: def __init__(self, env: gym.Env):
      self.env = env
      # We need to know in the method `space_to_maze` if the action space is a Dict action space.
      self._original_space_is_dict = isinstance(
          env.action_space, gym.spaces.Dict
      )
  
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 1 to create a new GymActionConversion object from inherited base classes: ActionConversionInterface with example: obj = GymActionConversion(env)