class_docstr: Compute `IR Recall at fixed Precision`_.
  
  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
  
  .. note:: All ``indexes``, ``preds`` and ``target`` must have the same dimension.
  
  .. note::
      Predictions will be first grouped by ``indexes`` and then `RetrievalRecallAtFixedPrecision`
      will be computed as the mean of the `RetrievalRecallAtFixedPrecision` over each query.
  
  As output to ``forward`` and ``compute`` the metric returns the following output:
  
  - ``max_recall`` (:class:`~torch.Tensor`): A tensor with the maximum recall value
    retrieved documents.
  - ``best_k`` (:class:`~torch.Tensor`): A tensor with the best k corresponding to the maximum recall value
  
  Args:
      min_precision: float value specifying minimum precision threshold.
      max_k: Calculate recall and precision for all possible top k from 1 to max_k
             (default: `None`, which considers all possible top k)
      adaptive_k: adjust `k` to `min(k, number of documents)` for each query
      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.
      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.
      ValueError:
          If ``min_precision`` parameter is not float or between 0 and 1.
      ValueError:
          If ``max_k`` parameter is not `None` or an integer larger than 0.
  
  Example:
      >>> from torch import tensor
      >>> from torchmetrics.retrieval import RetrievalRecallAtFixedPrecision
      >>> 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 = RetrievalRecallAtFixedPrecision(min_precision=0.8)
      >>> r(preds, target, indexes=indexes)
      (tensor(0.5000), tensor(1))
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 RetrievalRecallAtFixedPrecision
      >>> # Example plotting a single value
      >>> metric = RetrievalRecallAtFixedPrecision(min_precision=0.5)
      >>> 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 RetrievalRecallAtFixedPrecision
      >>> # Example plotting multiple values
      >>> metric = RetrievalRecallAtFixedPrecision(min_precision=0.5)
      >>> values = []
      >>> for _ in range(10):
      ...     values.append(metric(torch.rand(10,), torch.randint(2, (10,)), indexes=torch.randint(2,(10,)))[0])
      >>> fig, ax = metric.plot(values)
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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 RetrievalRecallAtFixedPrecision
          >>> # Example plotting a single value
          >>> metric = RetrievalRecallAtFixedPrecision(min_precision=0.5)
          >>> 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 RetrievalRecallAtFixedPrecision
          >>> # Example plotting multiple values
          >>> metric = RetrievalRecallAtFixedPrecision(min_precision=0.5)
          >>> values = []
          >>> for _ in range(10):
          ...     values.append(metric(torch.rand(10,), torch.randint(2, (10,)), indexes=torch.randint(2,(10,)))[0])
          >>> fig, ax = metric.plot(values)
  
      """
      val = val or self.compute()[0]
      return self._plot(val, ax)
  
imports: from typing import Any, Callable, List, Optional, Sequence, Tuple, Union
  import torch
  from torch import Tensor
  from typing_extensions import Literal
  from torchmetrics import Metric
  from torchmetrics.functional.retrieval.precision_recall_curve import retrieval_precision_recall_curve
  from torchmetrics.retrieval.base import _retrieval_aggregate
  from torchmetrics.utilities.checks import _check_retrieval_inputs
  from torchmetrics.utilities.data import _flexible_bincount, dim_zero_cat
  from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
  from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE, plot_curve
label_desc: Use image node_id 3 for calling the RetrievalRecallAtFixedPrecision obj's underlying member method code with example usage: obj.plot(val, ax) and returns: self