docstr: Sample a batch of experiences
docstr_tok: ["Sample","a","batch","of","experiences"]
code: def sample(self, batch_size):
      """Sample a batch of experiences"""
      idxes = self._sample_proportional(batch_size)
  
      it_sum = self._it_sum.sum()
      p_min = self._it_min.min() / it_sum
      max_weight = (p_min * len(self._storage)) ** (-self.beta)
  
      p_samples = (
          np.asarray([self._it_sum[idx] for idx in idxes]) / it_sum
      )
      weights = (p_samples * len(self._storage)) ** (
          -self.beta
      ) / max_weight
      encoded_sample = self._encode_sample(idxes)
      return encoded_sample + (weights.astype("float32"), idxes)
  
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 4 for calling the PrioritizedReplayBuffer obj's underlying member method code with example usage: obj.sample(batch_size) and returns: unknown