class_docstr: A DM Control Suite preprocessing wrapper.
  
  Attributes:
    environment: The underlying environment that is wrapped for preprocessing.
    game_over: True when the last environment step led to a game over signal.
class_docstr_tok: ["A","DM","Control","Suite","preprocessing","wrapper",".","Attributes",":","environment",":","The","underlying","environment","that","is","wrapped","for","preprocessing",".","game_over",":","True","when","the","last","environment","step","led","to","a","game","over","signal","."]
docstr: Steps the environment.
  
  Args:
    action: The action to apply.
  
  Returns:
    An (observation, reward, is_terminal, info) tuple. For more information,
    see base class.
docstr_tok: ["Steps","the","environment",".","Args",":","action",":","The","action","to","apply",".","Returns",":","An","(","observation",",","reward",",","is_terminal",",","info",")","tuple",".","For","more","information",",","see","base","class","."]
code: def step(
      self, action: np.ndarray
  ) -> Tuple[np.ndarray, float, bool, Mapping[Any, Any]]:
      """Steps the environment.
  
      Args:
        action: The action to apply.
  
      Returns:
        An (observation, reward, is_terminal, info) tuple. For more information,
        see base class.
      """
      accumulated_reward = 0.0
      self.game_over = False
      done = False
  
      for _ in range(self._action_repeat):
          timestep = self.environment.step(action)
          accumulated_reward += timestep.reward
  
          if timestep.last():
              self.game_over = True  # Signals end of episode.
  
              # Only set as terminal if not the result of a timeout.
              done = timestep.discount < 1.0
              break
  
      observation = self._get_observation(timestep)
  
      return observation, accumulated_reward, done, {}
  
imports: import collections
  from typing import Any, Mapping, Tuple
  from dm_control import suite
  from dm_control.rl import control
  import dm_env
  from dm_env import specs
  import gin
  import gym
  from gym import spaces
  import numpy
label_desc: Use image node_id 7 for calling the DeepmindControlPreprocessing obj's underlying member method code with example usage: obj.step(action) and returns: observation,  accumulated_reward,  done,  dict