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: Restores all local state to the provided `state`.
  
  Args:
      state: The new state to set this buffer. Can be obtained by calling
      `self.get_state()`.
docstr_tok: ["Restores","all","local","state","to","the","provided","`","state","`",".","Args",":","state",":","The","new","state","to","set","this","buffer",".","Can","be","obtained","by","calling","`","self.get_state","(",")","`","."]
code: def set_state(self, state: Dict[str, Any]) -> None:
      """Restores all local state to the provided `state`.
  
      Args:
          state: The new state to set this buffer. Can be obtained by calling
          `self.get_state()`.
      """
      super().set_state(state)
      self._it_sum.set_state(state["sum_segment_tree"])
      self._it_min.set_state(state["min_segment_tree"])
      self._max_priority = state["max_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 8 for calling the PrioritizedReplayBuffer obj's underlying member method code with example usage: obj.set_state(state) without return types