code: def build_dqn(num_actions, action_repeat):
      """
      Building a DQN.
      """
      inputs = tf.placeholder(tf.float32, [None, action_repeat, 84, 84])
      # Inputs shape: [batch, channel, height, width] need to be changed into
      # shape [batch, height, width, channel]
      net = tf.transpose(inputs, [0, 2, 3, 1])
      net = tflearn.conv_2d(net, 32, 8, strides=4, activation="relu")
      net = tflearn.conv_2d(net, 64, 4, strides=2, activation="relu")
      net = tflearn.fully_connected(net, 256, activation="relu")
      q_values = tflearn.fully_connected(net, num_actions)
      return inputs, q_values
  
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 for calling a global function with example usage: build_dqn(num_actions, action_repeat) and returns: inputs,  q_values