class_docstr: A class implementing image preprocessing for Atari 2600 agents.
  
  Specifically, this provides the following subset from the JAIR paper
  (Bellemare et al., 2013) and Nature DQN paper (Mnih et al., 2015):
  
    * Frame skipping (defaults to 4).
    * Terminal signal when a life is lost (off by default).
    * Grayscale and max-pooling of the last two frames.
    * Downsample the screen to a square image (defaults to 84x84).
  
  More generally, this class follows the preprocessing guidelines set down in
  Machado et al. (2018), "Revisiting the Arcade Learning Environment:
  Evaluation Protocols and Open Problems for General Agents".
docstr: Resets the environment.
  
  Returns:
    observation: numpy array, the initial observation emitted by the
      environment.
docstr_tok: ["Resets","the","environment",".","Returns",":","observation",":","numpy","array",",","the","initial","observation","emitted","by","the","environment","."]
code: def reset(self) -> np.ndarray:
      """Resets the environment.
  
      Returns:
        observation: numpy array, the initial observation emitted by the
          environment.
      """
      self.env.reset()
      self.lives = self.env.ale.lives()
      self.game_over = False
      self._fetch_grayscale_observation(self.screen_buffer[0])
      self.screen_buffer[1].fill(0)
      return self._pool_and_resize()
  
file_path: agents/tf_agents/environments/atari_preprocessing.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import cv2
  import gin
  import gym
  from gym import core
  from gym.spaces import box
  import numpy
label_desc: Use image node_id 2 for calling the AtariPreprocessing obj's underlying member method code with example usage: obj.reset() and returns: self