class_docstr: This buffer implements Prioritized Experience Replay.
  
  The algorithm has been described by Tom Schaul et. al. in "Prioritized
  Experience Replay". See https://arxiv.org/pdf/1511.05952.pdf for
  the full paper.
docstr: Update priorities of items at given indices.
  
  Sets priority of item at index idxes[i] in buffer
  to priorities[i].
  
  Args:
      idxes: List of indices of items
      priorities: List of updated priorities corresponding to items at the
      idxes denoted by variable `idxes`.
docstr_tok: ["Update","priorities","of","items","at","given","indices",".","Sets","priority","of","item","at","index","idxes","[","i","]","in","buffer","to","priorities","[","i","]",".","Args",":","idxes",":","List","of","indices","of","items","priorities",":","List","of","updated","priorities","corresponding","to","items","at","the","idxes","denoted","by","variable","`","idxes","`","."]
code: def update_priorities(
      self, idxes: List[int], priorities: List[float]
  ) -> None:
      """Update priorities of items at given indices.
  
      Sets priority of item at index idxes[i] in buffer
      to priorities[i].
  
      Args:
          idxes: List of indices of items
          priorities: List of updated priorities corresponding to items at the
          idxes denoted by variable `idxes`.
      """
      # Making sure we don't pass in e.g. a torch tensor.
      assert isinstance(
          idxes, (list, np.ndarray)
      ), "ERROR: `idxes` is not a list or np.ndarray, but {}!".format(
          type(idxes).__name__
      )
      assert len(idxes) == len(priorities)
      for idx, priority in zip(idxes, priorities):
          assert priority > 0
          assert 0 <= idx < len(self._storage)
          delta = priority**self._alpha - self._it_sum[idx]
          self._prio_change_stats.push(delta)
          self._it_sum[idx] = priority**self._alpha
          self._it_min[idx] = priority**self._alpha
  
          self._max_priority = max(self._max_priority, priority)
  
imports: import random
  from typing import Any, Dict, List, Optional
  import numpy
  import ray
  import psutil
  from ray.rllib.execution.segment_tree import SumSegmentTree, MinSegmentTree
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.metrics.window_stat import WindowStat
  from ray.rllib.utils.replay_buffers.replay_buffer import ReplayBuffer
  from ray.rllib.utils.typing import SampleBatchType
  from ray.util.annotations import DeveloperAPI
label_desc: Use image node_id 5 for calling the PrioritizedReplayBuffer obj's underlying member method code with example usage: obj.update_priorities(idxes, priorities) without return types