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: Returns all local state.
  
  Returns:
      The serializable local state.
docstr_tok: ["Returns","all","local","state",".","Returns",":","The","serializable","local","state","."]
code: def get_state(self) -> Dict[str, Any]:
      """Returns all local state.
  
      Returns:
          The serializable local state.
      """
      # Get parent state.
      state = super().get_state()
      # Add prio weights.
      state.update(
          {
              "sum_segment_tree": self._it_sum.get_state(),
              "min_segment_tree": self._it_min.get_state(),
              "max_priority": self._max_priority,
          }
      )
      return state
  
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 7 for calling the PrioritizedReplayBuffer obj's underlying member method code with example usage: obj.get_state() and returns: state