class_docstr: TimeLimit wrapper for continuing environments.
  
  This is similar gym.wrappers.TimeLimit, which sets a time limit for
  each episode, except that done=False is returned and that
  info['needs_reset'] is set to True when past the limit.
  
  Code that calls env.step is responsible for checking the info dict, the
  fourth returned value, and resetting the env if it has the 'needs_reset'
  key and its value is True.
  
  Args:
      env (gym.Env): Env to wrap.
      max_episode_steps (int): Maximum number of timesteps during an episode,
          after which the env needs a reset.
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code: def __init__(self, env, max_episode_steps):
      super(ContinuingTimeLimit, self).__init__(env)
      self._max_episode_steps = max_episode_steps
  
      self._elapsed_steps = None
  
imports: import gym
label_desc: Use image node_id 1 to create a new ContinuingTimeLimit object from inherited base classes: gym with example: obj = ContinuingTimeLimit(env, max_episode_steps)