class_docstr: Small wrapper for gym atari environments.
  Responsible for preprocessing screens and holding on to a screen buffer
  of size action_repeat from which environment state is constructed.
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docstr: 0) Atari frames: 210 x 160
  1) Get image grayscale
  2) Rescale image 110 x 84
  3) Crop center 84 x 84 (you can crop top/bottom according to the game)
docstr_tok: ["0",")","Atari","frames",":","210","x","160","1",")","Get","image","grayscale","2",")","Rescale","image","110","x","84","3",")","Crop","center","84","x","84","(","you","can","crop","top\/bottom","according","to","the","game",")"]
code: def get_preprocessed_frame(self, observation):
      """
      0) Atari frames: 210 x 160
      1) Get image grayscale
      2) Rescale image 110 x 84
      3) Crop center 84 x 84 (you can crop top/bottom according to the game)
      """
      return resize(rgb2gray(observation), (110, 84))[13 : 110 - 13, :]
  
imports: from __future__ import division, print_function, absolute_import
  import threading
  import random
  import numpy
  import time
  from skimage.transform import resize
  from skimage.color import rgb2gray
  from collections import deque
  import gym
  import tensorflow.compat.v1
  import tflearn
label_desc: Use image node_id 3 for calling the AtariEnvironment obj's underlying member method code with example usage: obj.get_preprocessed_frame(observation) and returns: resize