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.
class_docstr_tok: ["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","."]
code: def __init__(self, gym_env, action_repeat):
      self.env = gym_env
      self.action_repeat = action_repeat
  
      # Agent available actions, such as LEFT, RIGHT, NOOP, etc...
      self.gym_actions = range(gym_env.action_space.n)
      # Screen buffer of size action_repeat to be able to build
      # state arrays of size [1, action_repeat, 84, 84]
      self.state_buffer = deque()
  
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 1 to create a new AtariEnvironment object from inherited base classes: object with example: obj = AtariEnvironment(gym_env, action_repeat)