code: def _train_func(self, b_o, b_index, b_m):
      with tf.GradientTape() as tape:
          b_dist_a = tf.gather_nd(self.qnet(b_o), b_index)
          loss = tf.reduce_mean(
              tf.negative(tf.reduce_sum(b_dist_a * b_m, 1))
          )
  
      grad = tape.gradient(loss, self.qnet.trainable_weights)
      self.optimizer.apply_gradients(
          zip(grad, self.qnet.trainable_weights)
      )
  
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 7 for calling the DQN obj's underlying member method code with example usage: obj._train_func(b_o, b_index, b_m) without return types