class_docstr: A wrapper for ALE emulator, with configurations to mimic DeepMind DQN settings.
  
  Info:
      score: the accumulated reward in the current game
      gameOver: True when the current game is Over
class_docstr_tok: ["A","wrapper","for","ALE","emulator",",","with","configurations","to","mimic","DeepMind","DQN","settings",".","Info",":","score",":","the","accumulated","reward","in","the","current","game","gameOver",":","True","when","the","current","game","is","Over"]
code: def _restart_episode(self):
      with _ALE_LOCK:
          self.ale.reset_game()
  
      # random null-ops start
      n = self.rng.randint(self.nullop_start)
      self.last_raw_screen = self._grab_raw_image()
      for k in range(n):
          if k == n - 1:
              self.last_raw_screen = self._grab_raw_image()
          self.ale.act(0)
  
file_path: tensorpack/examples/DeepQNetwork/atari.py
imports: import numpy
  import os
  import threading
  import cv2
  import gym
  import six
  from ale_py import ALEInterface, Action, LoggerMode
  from gym import spaces
  from tensorpack.utils import logger, execute_only_once, get_rng
  from tensorpack.utils.fs import get_dataset_path
label_desc: Use image node_id 5 for calling the AtariPlayer obj's underlying member method code with example usage: obj._restart_episode() without return types