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: Add a batch of experiences to self._storage with weight.
  
  An item consists of either one or more timesteps, a sequence or an
  episode. Differs from add() in that it does not consider the storage
  unit or type of batch and simply stores it.
  
  Args:
      item: The item to be added.
      ``**kwargs``: Forward compatibility kwargs.
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code: def _add_single_batch(self, item: SampleBatchType, **kwargs) -> None:
      """Add a batch of experiences to self._storage with weight.
  
      An item consists of either one or more timesteps, a sequence or an
      episode. Differs from add() in that it does not consider the storage
      unit or type of batch and simply stores it.
  
      Args:
          item: The item to be added.
          ``**kwargs``: Forward compatibility kwargs.
      """
      weight = kwargs.get("weight", None)
  
      if weight is None:
          weight = self._max_priority
  
      self._it_sum[self._next_idx] = weight**self._alpha
      self._it_min[self._next_idx] = weight**self._alpha
  
      ReplayBuffer._add_single_batch(self, item)
  
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 2 for calling the PrioritizedReplayBuffer obj's underlying member method code with example usage: obj._add_single_batch(item) without return types