code: def __init__(self):
      model = MLP if qnet_type == "MLP" else CNN
      self.qnet = model("q")
      if args.train:
          self.qnet.train()
          self.targetqnet = model("targetq")
          self.targetqnet.infer()
          sync(self.qnet, self.targetqnet)
      else:
          self.qnet.infer()
          self.load(args.save_path)
      self.niter = 0
      if clipnorm is not None:
          self.optimizer = tf.optimizers.Adam(
              learning_rate=lr, clipnorm=clipnorm
          )
      else:
          self.optimizer = tf.optimizers.Adam(learning_rate=lr)
  
imports: import argparse
  import operator
  import os
  import random
  import time
  import gym
  import matplotlib.pyplot
  import numpy
  import tensorflow
  import tensorlayer
label_desc: Use image node_id 1 to create a new DQN object from inherited base classes: object with example: obj = DQN()