docstr: Update priorities of sampled transitions.
  
  sets priority of transition at index idxes[i] in buffer
  to priorities[i].
  
  :param idxes: ([int]) List of idxes of sampled transitions
  :param priorities: ([float]) List of updated priorities corresponding to transitions at the sampled idxes
      denoted by variable `idxes`.
docstr_tok: ["Update","priorities","of","sampled","transitions",".","sets","priority","of","transition","at","index","idxes","[","i","]","in","buffer","to","priorities","[","i","]",".",":","param","idxes",":","(","[","int","]",")","List","of","idxes","of","sampled","transitions",":","param","priorities",":","(","[","float","]",")","List","of","updated","priorities","corresponding","to","transitions","at","the","sampled","idxes","denoted","by","variable","`","idxes","`","."]
code: def update_priorities(self, idxes, priorities):
      """
      Update priorities of sampled transitions.
  
      sets priority of transition at index idxes[i] in buffer
      to priorities[i].
  
      :param idxes: ([int]) List of idxes of sampled transitions
      :param priorities: ([float]) List of updated priorities corresponding to transitions at the sampled idxes
          denoted by variable `idxes`.
      """
      assert len(idxes) == len(priorities)
      assert np.min(priorities) > 0
      assert np.min(idxes) >= 0
      assert np.max(idxes) < len(self.storage)
      self._it_sum[idxes] = priorities**self._alpha
      self._it_min[idxes] = priorities**self._alpha
  
      self._max_priority = max(self._max_priority, np.max(priorities))
  
imports: import random
  from typing import Optional, List, Union
  import numpy
  from stable_baselines.common.segment_tree import SumSegmentTree, MinSegmentTree
  from stable_baselines.common.vec_env import VecNormalize
label_desc: Use image node_id 6 for calling the PrioritizedReplayBuffer obj's underlying member method code with example usage: obj.update_priorities(idxes, priorities) without return types