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".
  It also provides random starting no-ops, which are used in the Rainbow, Apex
  and R2D2 papers.
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code: def close(self):
      return self.environment.close()
  
file_path: flax/examples/ppo/seed_rl_atari_preprocessing.py
imports: import cv2
  from gymnasium.spaces.box import Box
  import gymnasium
  import numpy
label_desc: Use image node_id 6 for calling the AtariPreprocessing obj's underlying member method code with example usage: obj.close() and returns: self