code: def _train_func(self, b_o, b_a, b_r, b_o_, b_d):
      with tf.GradientTape() as tape:
          td_errors = self._tderror_func(b_o, b_a, b_r, b_o_, b_d)
          loss = tf.reduce_mean(huber_loss(td_errors))
  
      grad = tape.gradient(loss, self.qnet.trainable_weights)
      self.optimizer.apply_gradients(
          zip(grad, self.qnet.trainable_weights)
      )
  
      return td_errors
  
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 5 for calling the DQN obj's underlying member method code with example usage: obj._train_func(b_o, b_a, b_r, b_o_, b_d) and returns: td_errors