code: def get_action(self, obv):
      eps = epsilon(self.niter)
      if args.train:
          if random.random() < eps:
              return int(random.random() * out_dim)
          obv = np.expand_dims(obv, 0).astype("float32") * ob_scale
          if self.niter < explore_timesteps:
              self.qnet.noise_scale = self.noise_scale
              q_ptb = self._qvalues_func(obv).numpy()
              self.qnet.noise_scale = 0
              if i % noise_update_freq == 0:
                  q = self._qvalues_func(obv).numpy()
                  kl_ptb = log_softmax(q, 1) - log_softmax(q_ptb, 1)
                  kl_ptb = np.sum(kl_ptb * softmax(q, 1), 1).mean()
                  kl_explore = -np.log(1 - eps + eps / out_dim)
                  if kl_ptb < kl_explore:
                      self.noise_scale *= 1.01
                  else:
                      self.noise_scale /= 1.01
              return q_ptb.argmax(1)[0]
          else:
              return self._qvalues_func(obv).numpy().argmax(1)[0]
      else:
          obv = np.expand_dims(obv, 0).astype("float32") * ob_scale
          return self._qvalues_func(obv).numpy().argmax(1)[0]
  
imports: import argparse
  import os
  import random
  import time
  import gym
  import matplotlib.pyplot
  import numpy
  import tensorflow
  import tensorlayer
label_desc: Use image node_id 2 for calling the DQN obj's underlying member method code with example usage: obj.get_action(obv) and returns: self, int, q_ptb, self