class_docstr: Environment wrapper that stops episode at step max_episode_length.
class_docstr_tok: ["Environment","wrapper","that","stops","episode","at","step","max_episode_length","."]
docstr: Step the wrapped environment.
  
  Args:
      action (np.ndarray): the action.
  
  Returns:
      np.ndarray: Next observation
      float: Reward
      bool: Termination signal
      dict: Environment information
docstr_tok: ["Step","the","wrapped","environment",".","Args",":","action","(","np.ndarray",")",":","the","action",".","Returns",":","np.ndarray",":","Next","observation","float",":","Reward","bool",":","Termination","signal","dict",":","Environment","information"]
code: def step(self, action):
      """Step the wrapped environment.
  
      Args:
          action (np.ndarray): the action.
  
      Returns:
          np.ndarray: Next observation
          float: Reward
          bool: Termination signal
          dict: Environment information
      """
      self._episode_step += 1
      next_obs, reward, done, info = self.env.step(action)
      if self._episode_step == self._max_episode_length:
          done = True
          self._episode_step = 0
      return next_obs, reward, done, info
  
imports: import gym
label_desc: Use image node_id 2 for calling the AutoStopEnv obj's underlying member method code with example usage: obj.step(action) and returns: next_obs,  reward,  done,  info