class_docstr: A prioritized replay buffer shard for multiagent setups.
  
  This buffer is meant to be run in parallel to distribute experiences
  across `num_shards` shards. Unlike simpler buffers, it holds a set of
  buffers - one for each policy ID.
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docstr: Updates the priorities of underlying replay buffers.
  
  Computes new priorities from td_errors and prioritized_replay_eps.
  These priorities are used to update underlying replay buffers per
  policy_id.
  
  Args:
      prio_dict: A dictionary containing td_errors for
      batches saved in underlying replay buffers.
docstr_tok: ["Updates","the","priorities","of","underlying","replay","buffers",".","Computes","new","priorities","from","td_errors","and","prioritized_replay_eps",".","These","priorities","are","used","to","update","underlying","replay","buffers","per","policy_id",".","Args",":","prio_dict",":","A","dictionary","containing","td_errors","for","batches","saved","in","underlying","replay","buffers","."]
code: def update_priorities(self, prio_dict: Dict) -> None:
      """Updates the priorities of underlying replay buffers.
  
      Computes new priorities from td_errors and prioritized_replay_eps.
      These priorities are used to update underlying replay buffers per
      policy_id.
  
      Args:
          prio_dict: A dictionary containing td_errors for
          batches saved in underlying replay buffers.
      """
      with self.update_priorities_timer:
          for policy_id, (
              batch_indexes,
              td_errors,
          ) in prio_dict.items():
              new_priorities = (
                  np.abs(td_errors) + self.prioritized_replay_eps
              )
              self.replay_buffers[policy_id].update_priorities(
                  batch_indexes, new_priorities
              )
  
imports: from typing import Dict
  import logging
  import numpy
  from ray.util.timer import _Timer
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.replay_buffers.multi_agent_replay_buffer import MultiAgentReplayBuffer, ReplayMode, merge_dicts_with_warning
  from ray.rllib.utils.replay_buffers.prioritized_replay_buffer import PrioritizedReplayBuffer
  from ray.rllib.utils.replay_buffers.replay_buffer import StorageUnit
  from ray.rllib.utils.typing import PolicyID, SampleBatchType
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.util.debug import log_once
  from ray.util.annotations import DeveloperAPI
  from ray.rllib.policy.rnn_sequencing import timeslice_along_seq_lens_with_overlap
label_desc: Use image node_id 3 for calling the MultiAgentPrioritizedReplayBuffer obj's underlying member method code with example usage: obj.update_priorities(prio_dict) without return types