code: def sample(self, batch_size, beta):
      indices = []
      max_value = self.sum_tree.root
      for _ in range(batch_size):
          value = np.random.uniform(0, max_value)
          idx = self.sum_tree.sample(value)
          indices.append(idx)
      min_value = self.min_tree.root
      return (
          indices,
          self.memory[indices],
          (self.sum_tree[indices] / (min_value + 1e-4)) ** (-beta),
      )
  
file_path: dynet/examples/reinforcement-learning/memory.py
imports: import numpy
  from math import log, ceil
  from reduce_tree import ReduceTree, SumTree
label_desc: Use image node_id 3 for calling the PrioritizedMemory obj's underlying member method code with example usage: obj.sample(batch_size, beta) and returns: indices,  self,  unknown