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: Compute metric.
docstr_tok: ["Compute","metric","."]
code: def compute(self) -> Tuple[Tensor, Tensor]:  # type: ignore[override]
      """Compute metric."""
      precisions, recalls, top_k = super().compute()
  
      return _retrieval_recall_at_fixed_precision(
          precisions, recalls, top_k, self.min_precision
      )
  
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 2 for calling the RetrievalRecallAtFixedPrecision obj's underlying member method code with example usage: obj.compute() and returns: _retrieval_recall_at_fixed_precision