class_docstr: Compute the highest possible recall value given the minimum precision thresholds provided.
  
  This is done by first calculating the precision-recall curve for different thresholds and the find the recall for
  a given precision level.
  
  For multiclass the metric is calculated by iteratively treating each class as the positive class and all other
  classes as the negative, which is referred to as the one-vs-rest approach. One-vs-one is currently not supported by
  this metric.
  
  As input to ``forward`` and ``update`` the metric accepts the following input:
  
  - ``preds`` (:class:`~torch.Tensor`): A float tensor of shape ``(N, C, ...)``. Preds should be a tensor
    containing probabilities or logits for each observation. If preds has values outside [0,1] range we consider
    the input to be logits and will auto apply softmax per sample.
  - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``. Target should be a tensor containing
    ground truth labels, and therefore only contain values in the [0, n_classes-1] range (except if `ignore_index`
    is specified).
  
  .. note::
     Additional dimension ``...`` will be flattened into the batch dimension.
  
  As output to ``forward`` and ``compute`` the metric returns a tuple of either 2 tensors or 2 lists containing:
  
  - ``recall`` (:class:`~torch.Tensor`): A 1d tensor of size ``(n_classes, )`` with the maximum recall for the
    given precision level per class
  - ``threshold`` (:class:`~torch.Tensor`): A 1d tensor of size ``(n_classes, )`` with the corresponding threshold
    level per class
  
  .. note::
     The implementation both supports calculating the metric in a non-binned but accurate version and a binned version
     that is less accurate but more memory efficient. Setting the `thresholds` argument to ``None`` will activate the
     non-binned  version that uses memory of size :math:`\mathcal{O}(n_{samples})` whereas setting the `thresholds`
     argument to either an integer, list or a 1d tensor will use a binned version that uses memory of
     size :math:`\mathcal{O}(n_{thresholds} \times n_{classes})` (constant memory).
  
  Args:
      num_classes: Integer specifying the number of classes
      min_precision: float value specifying minimum precision threshold.
      thresholds:
          Can be one of:
  
          - If set to `None`, will use a non-binned approach where thresholds are dynamically calculated from
            all the data. Most accurate but also most memory consuming approach.
          - If set to an ``int`` (larger than 1), will use that number of thresholds linearly spaced from
            0 to 1 as bins for the calculation.
          - If set to an ``list`` of floats, will use the indicated thresholds in the list as bins for the calculation
          - If set to an 1d :class:`~torch.Tensor` of floats, will use the indicated thresholds in the tensor as
            bins for the calculation.
  
      validate_args: bool indicating if input arguments and tensors should be validated for correctness.
          Set to ``False`` for faster computations.
      kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
  
  Example:
      >>> from torch import tensor
      >>> from torchmetrics.classification import MulticlassRecallAtFixedPrecision
      >>> preds = tensor([[0.75, 0.05, 0.05, 0.05, 0.05],
      ...                 [0.05, 0.75, 0.05, 0.05, 0.05],
      ...                 [0.05, 0.05, 0.75, 0.05, 0.05],
      ...                 [0.05, 0.05, 0.05, 0.75, 0.05]])
      >>> target = tensor([0, 1, 3, 2])
      >>> metric = MulticlassRecallAtFixedPrecision(num_classes=5, min_precision=0.5, thresholds=None)
      >>> metric(preds, target)
      (tensor([1., 1., 0., 0., 0.]), tensor([7.5000e-01, 7.5000e-01, 1.0000e+06, 1.0000e+06, 1.0000e+06]))
      >>> mcrafp = MulticlassRecallAtFixedPrecision(num_classes=5, min_precision=0.5, thresholds=5)
      >>> mcrafp(preds, target)
      (tensor([1., 1., 0., 0., 0.]), tensor([7.5000e-01, 7.5000e-01, 1.0000e+06, 1.0000e+06, 1.0000e+06]))
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docstr: Compute metric.
docstr_tok: ["Compute","metric","."]
code: def compute(self) -> Tuple[Tensor, Tensor]:  # type: ignore[override]
      """Compute metric."""
      state = (
          (dim_zero_cat(self.preds), dim_zero_cat(self.target))
          if self.thresholds is None
          else self.confmat
      )
      return _multiclass_recall_at_fixed_precision_arg_compute(
          state, self.num_classes, self.thresholds, self.min_precision
      )
  
imports: from typing import Any, List, Optional, Sequence, Tuple, Type, Union
  from torch import Tensor
  from typing_extensions import Literal
  from torchmetrics.classification.base import _ClassificationTaskWrapper
  from torchmetrics.classification.precision_recall_curve import BinaryPrecisionRecallCurve, MulticlassPrecisionRecallCurve, MultilabelPrecisionRecallCurve
  from torchmetrics.functional.classification.recall_fixed_precision import _binary_recall_at_fixed_precision_arg_validation, _binary_recall_at_fixed_precision_compute, _multiclass_recall_at_fixed_precision_arg_compute, _multiclass_recall_at_fixed_precision_arg_validation, _multilabel_recall_at_fixed_precision_arg_compute, _multilabel_recall_at_fixed_precision_arg_validation
  from torchmetrics.metric import Metric
  from torchmetrics.utilities.data import dim_zero_cat
  from torchmetrics.utilities.enums import ClassificationTask
  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 MulticlassRecallAtFixedPrecision obj's underlying member method code with example usage: obj.compute() and returns: _multiclass_recall_at_fixed_precision_arg_compute