class_docstr: Compute `IR R-Precision`_.
  
  Works with binary target data. Accepts float predictions from a model output.
  
  As input to ``forward`` and ``update`` the metric accepts the following input:
  
  - ``preds`` (:class:`~torch.Tensor`): A float tensor of shape ``(N, ...)``
  - ``target`` (:class:`~torch.Tensor`): A long or bool tensor of shape ``(N, ...)``
  - ``indexes`` (:class:`~torch.Tensor`): A long tensor of shape ``(N, ...)`` which indicate to which query a
    prediction belongs
  
  As output to ``forward`` and ``compute`` the metric returns the following output:
  
  - ``rp`` (:class:`~torch.Tensor`): A single-value tensor with the r-precision of the predictions ``preds``
    w.r.t. the labels ``target``.
  
  All ``indexes``, ``preds`` and ``target`` must have the same dimension and will be flatten at the beginning,
  so that for example, a tensor of shape ``(N, M)`` is treated as ``(N * M, )``. Predictions will be first grouped by
  ``indexes`` and then will be computed as the mean of the metric over each query.
  
  Args:
      empty_target_action:
          Specify what to do with queries that do not have at least a positive ``target``. Choose from:
  
          - ``'neg'``: those queries count as ``0.0`` (default)
          - ``'pos'``: those queries count as ``1.0``
          - ``'skip'``: skip those queries; if all queries are skipped, ``0.0`` is returned
          - ``'error'``: raise a ``ValueError``
  
      ignore_index: Ignore predictions where the target is equal to this number.
      aggregation:
          Specify how to aggregate over indexes. Can either a custom callable function that takes in a single tensor
          and returns a scalar value or one of the following strings:
  
          - ``'mean'``: average value is returned
          - ``'median'``: median value is returned
          - ``'max'``: max value is returned
          - ``'min'``: min value is returned
  
      kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
  
  Raises:
      ValueError:
          If ``empty_target_action`` is not one of ``error``, ``skip``, ``neg`` or ``pos``.
      ValueError:
          If ``ignore_index`` is not `None` or an integer.
  
  Example:
      >>> from torch import tensor
      >>> from torchmetrics.retrieval import RetrievalRPrecision
      >>> indexes = tensor([0, 0, 0, 1, 1, 1, 1])
      >>> preds = tensor([0.2, 0.3, 0.5, 0.1, 0.3, 0.5, 0.2])
      >>> target = tensor([False, False, True, False, True, False, True])
      >>> p2 = RetrievalRPrecision()
      >>> p2(preds, target, indexes=indexes)
      tensor(0.7500)
code: def _metric(self, preds: Tensor, target: Tensor) -> Tensor:
      return retrieval_r_precision(preds, target)
  
file_path: torchmetrics/src/torchmetrics/retrieval/r_precision.py
imports: from typing import Optional, Sequence, Union
  from torch import Tensor
  from torchmetrics.functional.retrieval.r_precision import retrieval_r_precision
  from torchmetrics.retrieval.base import RetrievalMetric
  from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
  from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
label_desc: Use image node_id 1 for calling the RetrievalRPrecision obj's underlying member method code with example usage: obj._metric(preds, target) and returns: retrieval_r_precision