code: def _sample_proportional(self, batch_size):
      res = []
      p_total = self._it_sum.sum(0, len(self._storage) - 1)
      every_range_len = p_total / batch_size
      for i in range(batch_size):
          mass = random.random() * every_range_len + i * every_range_len
          idx = self._it_sum.find_prefixsum_idx(mass)
          res.append(idx)
      return res
  
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 3 for calling the PrioritizedReplayBuffer obj's underlying member method code with example usage: obj._sample_proportional(batch_size) and returns: res