code: def __init__(self, env):
      gym.Wrapper.__init__(self, env)
      self.action_space = env.action_space
      self.timeout_space = gym.spaces.Box(
          low=np.array([0.0]), high=np.array([1.0]), dtype=np.float32
      )
      self.original_os = env.observation_space
      if isinstance(self.original_os, gym.spaces.Dict):
          import copy
  
          ordered_dict = copy.deepcopy(self.original_os.spaces)
          ordered_dict["value_estimation_timeout"] = self.timeout_space
          self.observation_space = gym.spaces.Dict(ordered_dict)
          self.dict_mode = True
      else:
          self.observation_space = gym.spaces.Dict(
              {
                  "original": self.original_os,
                  "value_estimation_timeout": self.timeout_space,
              }
          )
          self.dict_mode = False
      self.ac_count = None
      while 1:
          if not hasattr(
              env, "_max_episode_steps"
          ):  # Looking for TimeLimit wrapper that has this field
              env = env.env
              continue
          break
      self.timeout = env._max_episode_steps
  
imports: from collections import deque
  import cv2
  from .atari_wrappers import WarpFrame, ClipRewardEnv, FrameStack, ScaledFloatFrame
  from .wrappers import TimeLimit
  import numpy
  import gym
label_desc: Use image node_id 1 to create a new AppendTimeout object from inherited base classes: gym with example: obj = AppendTimeout(env)