class_docstr: Wrapper for deprecated import.
  
  >>> from torch import tensor
  >>> indexes = tensor([0, 0, 0, 0, 1, 1, 1])
  >>> preds = tensor([0.4, 0.01, 0.5, 0.6, 0.2, 0.3, 0.5])
  >>> target = tensor([True, False, False, True, True, False, True])
  >>> r = _RetrievalPrecisionRecallCurve(max_k=4)
  >>> precisions, recalls, top_k = r(preds, target, indexes=indexes)
  >>> precisions
  tensor([1.0000, 0.5000, 0.6667, 0.5000])
  >>> recalls
  tensor([0.5000, 0.5000, 1.0000, 1.0000])
  >>> top_k
  tensor([1, 2, 3, 4])
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code: def __init__(
      self,
      max_k: Optional[int] = None,
      adaptive_k: bool = False,
      empty_target_action: str = "neg",
      ignore_index: Optional[int] = None,
      **kwargs: Any,
  ) -> None:
      _deprecated_root_import_class("", "retrieval")
      super().__init__(
          max_k=max_k,
          adaptive_k=adaptive_k,
          empty_target_action=empty_target_action,
          ignore_index=ignore_index,
          **kwargs,
      )
  
imports: from typing import Any, Optional
  from torchmetrics.retrieval.average_precision import RetrievalMAP
  from torchmetrics.retrieval.fall_out import RetrievalFallOut
  from torchmetrics.retrieval.hit_rate import RetrievalHitRate
  from torchmetrics.retrieval.ndcg import RetrievalNormalizedDCG
  from torchmetrics.retrieval.precision import RetrievalPrecision
  from torchmetrics.retrieval.precision_recall_curve import RetrievalPrecisionRecallCurve, RetrievalRecallAtFixedPrecision
  from torchmetrics.retrieval.r_precision import RetrievalRPrecision
  from torchmetrics.retrieval.recall import RetrievalRecall
  from torchmetrics.retrieval.reciprocal_rank import RetrievalMRR
  from torchmetrics.utilities.prints import _deprecated_root_import_class
label_desc: Use image node_id 1 to create a new _RetrievalPrecisionRecallCurve object from inherited base classes: RetrievalPrecisionRecallCurve with example: obj = _RetrievalPrecisionRecallCurve(max_k, adaptive_k, empty_target_action, ignore_index)