code: def train(self, b_o, b_a, b_r, b_o_, b_d):
      # TODO: move q_estimation in tf.function
      b_dist_ = np.exp(self.targetqnet(b_o_).numpy())
      b_a_ = (b_dist_ * vrange).sum(-1).argmax(1)
      b_tzj = np.clip(
          reward_gamma * (1 - b_d[:, None]) * vrange[None, :]
          + b_r[:, None],
          min_value,
          max_value,
      )
      b_i = (b_tzj - min_value) / deltaz
      b_l = np.floor(b_i).astype("int64")
      b_u = np.ceil(b_i).astype("int64")
      templ = b_dist_[range(batch_size), b_a_, :] * (b_u - b_i)
      tempu = b_dist_[range(batch_size), b_a_, :] * (b_i - b_l)
      b_m = np.zeros((batch_size, atom_num))
      # TODO: aggregate value by index and batch update (scatter_add)
      for j in range(batch_size):
          for k in range(atom_num):
              b_m[j][b_l[j][k]] += templ[j][k]
              b_m[j][b_u[j][k]] += tempu[j][k]
      b_m = tf.convert_to_tensor(b_m, dtype="float32")
      b_index = np.stack([range(batch_size), b_a], 1)
      b_index = tf.convert_to_tensor(b_index, "int64")
  
      self._train_func(b_o, b_index, b_m)
  
      self.niter += 1
      if self.niter % target_q_update_freq == 0:
          sync(self.qnet, self.targetqnet)
          self.save(args.save_path)
  
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 4 for calling the DQN obj's underlying member method code with example usage: obj.train(b_o, b_a, b_r, b_o_, b_d) without return types