class_docstr: Stochastic Prioritization
  
  https://arxiv.org/pdf/1511.05952.pdf Section 3.3
  proportional prioritization
  
  Args:
      capacity (int): capacity in terms of number of transitions
      alpha (float): Exponent of errors to compute probabilities to sample
      beta0 (float): Initial value of beta
      betasteps (int): Steps to anneal beta to 1
      eps (float): To revisit a step after its error becomes near zero
      normalize_by_max (bool): Method to normalize weights. ``'batch'`` or
          ``True`` (default): divide by the maximum weight in the sampled
          batch. ``'memory'``: divide by the maximum weight in the memory.
          ``False``: do not normalize
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code: def update_errors(self, errors):
      self.memory.set_last_priority(self.priority_from_errors(errors))
  
file_path: chainerrl/chainerrl/replay_buffers/prioritized.py
imports: import collections
  import numpy
  from chainerrl.misc.prioritized import PrioritizedBuffer
  from chainerrl.replay_buffers.replay_buffer import ReplayBuffer
label_desc: Use image node_id 3 for calling the PrioritizedReplayBuffer obj's underlying member method code with example usage: obj.update_errors(errors) without return types