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)
docstr: Plot a single or multiple values from the metric.
  
  Args:
      val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
          If no value is provided, will automatically call `metric.compute` and plot that result.
      ax: An matplotlib axis object. If provided will add plot to that axis
  
  Returns:
      Figure and Axes object
  
  Raises:
      ModuleNotFoundError:
          If `matplotlib` is not installed
  
  .. plot::
      :scale: 75
  
      >>> import torch
      >>> from torchmetrics.retrieval import RetrievalRPrecision
      >>> # Example plotting a single value
      >>> metric = RetrievalRPrecision()
      >>> metric.update(torch.rand(10,), torch.randint(2, (10,)), indexes=torch.randint(2,(10,)))
      >>> fig_, ax_ = metric.plot()
  
  .. plot::
      :scale: 75
  
      >>> import torch
      >>> from torchmetrics.retrieval import RetrievalRPrecision
      >>> # Example plotting multiple values
      >>> metric = RetrievalRPrecision()
      >>> values = []
      >>> for _ in range(10):
      ...     values.append(metric(torch.rand(10,), torch.randint(2, (10,)), indexes=torch.randint(2,(10,))))
      >>> fig, ax = metric.plot(values)
code: def plot(
      self,
      val: Optional[Union[Tensor, Sequence[Tensor]]] = None,
      ax: Optional[_AX_TYPE] = None,
  ) -> _PLOT_OUT_TYPE:
      """Plot a single or multiple values from the metric.
  
      Args:
          val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
              If no value is provided, will automatically call `metric.compute` and plot that result.
          ax: An matplotlib axis object. If provided will add plot to that axis
  
      Returns:
          Figure and Axes object
  
      Raises:
          ModuleNotFoundError:
              If `matplotlib` is not installed
  
      .. plot::
          :scale: 75
  
          >>> import torch
          >>> from torchmetrics.retrieval import RetrievalRPrecision
          >>> # Example plotting a single value
          >>> metric = RetrievalRPrecision()
          >>> metric.update(torch.rand(10,), torch.randint(2, (10,)), indexes=torch.randint(2,(10,)))
          >>> fig_, ax_ = metric.plot()
  
      .. plot::
          :scale: 75
  
          >>> import torch
          >>> from torchmetrics.retrieval import RetrievalRPrecision
          >>> # Example plotting multiple values
          >>> metric = RetrievalRPrecision()
          >>> values = []
          >>> for _ in range(10):
          ...     values.append(metric(torch.rand(10,), torch.randint(2, (10,)), indexes=torch.randint(2,(10,))))
          >>> fig, ax = metric.plot(values)
  
      """
      return self._plot(val, ax)
  
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 2 for calling the RetrievalRPrecision obj's underlying member method code with example usage: obj.plot(val, ax) and returns: self