code: def update(self, indices, values):
      values = np.array(values)
      values_modified = values**self.alpha
      self.sum_tree[indices] = values_modified
      self.min_tree[indices] = values_modified
      self.max_value = max(self.max_value, np.max(values))
      self.max_value = min(self.max_value, self.max_value_upper)
  
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 4 for calling the PrioritizedMemory obj's underlying member method code with example usage: obj.update(indices, values) without return types