class_docstr: The Arcade Learning Environment with popular settings.
  
  This mimics the environments used by the DQN paper from DeepMind,
  https://www.nature.com/articles/nature14236.
  
  Args:
      game (str): Name of a game. You can get the complete list of supported
          games by calling atari_py.list_games().
      seed (int or None): If set to an int, it is used as a random seed for
          the ALE. It must be in [0, 2 ** 31). If set to None, numpy's random
          state is used to select a random seed for the ALE.
      use_sdl (bool): If set to True, use SDL to show a window to render
          states. This option might not work for the ALE in atari_py.
      n_last_screens (int): Number of last screens to observe every step.
      frame_skip (int): Number of frames for which the same action is
          repeated. For example, if it is set to 4, one step for the agent
          corresponds to four frames in the ALE.
      crop_or_scale (str): How screens are resized. If set to 'crop', screens
          are cropped as in https://arxiv.org/abs/1312.5602. If set to
          'scale', screens are scaled as in
          https://www.nature.com/articles/nature14236.
      max_start_nullops (int): Maximum number of random null actions sent to
          the ALE to randomize initial states.
      record_screen_dir (str): If set to a str, screens are saved as images
          to the directory specified by it. If set to None, screens are not
          saved.
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code: def reset(self):
      self.initialize()
      return self.state
  
imports: import collections
  import os
  import sys
  import warnings
  from gym import spaces
  import numpy
  from chainerrl import env
label_desc: Use image node_id 9 for calling the ALE obj's underlying member method code with example usage: obj.reset() and returns: self