class_docstr: For proportional prioritization
  
  alpha determines how much prioritization is used.
  
  beta determines how much importance sampling weights are used. beta is
  scheduled by ``beta0`` and ``betasteps``.
  
  Args:
      alpha (float): Exponent of errors to compute probabilities to sample
      beta0 (float): Initial value of beta
      betasteps (float): Steps to anneal beta to 1
      eps (float): To revisit a step after its error becomes near zero
      normalize_by_max (str): 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 priority_from_errors(self, errors):
      def _clip_error(error):
          if self.error_min is not None:
              error = max(self.error_min, error)
          if self.error_max is not None:
              error = min(self.error_max, error)
          return error
  
      return [(_clip_error(d) + self.eps) ** self.alpha for d in errors]
  
file_path: pfrl/pfrl/replay_buffers/prioritized.py
imports: import collections
  import numpy
  from pfrl.collections.prioritized import PrioritizedBuffer
  from pfrl.replay_buffers.replay_buffer import ReplayBuffer
label_desc: Use image node_id 2 for calling the PriorityWeightError obj's underlying member method code with example usage: obj.priority_from_errors(errors) and returns: unknown, error