class_docstr: Compute the highest possible precision value given the minimum recall thresholds provided.
  
  This is done by first calculating the precision-recall curve for different thresholds and the find the precision for
  a given recall level.
  
  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 sigmoid per element.
  - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``. Target should be a tensor containing
    ground truth labels, and therefore only contain {0,1} values (except if `ignore_index` is specified). The value
    1 always encodes the positive class.
  
  .. 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:
  
  - ``precision`` (:class:`~torch.Tensor`): A 1d tensor of size ``(n_classes, )`` with the maximum precision for the
    given recall 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_{labels})` (constant memory).
  
  Args:
      num_labels: Integer specifying the number of labels
      min_recall: float value specifying minimum recall 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 MultilabelPrecisionAtFixedRecall
      >>> preds = tensor([[0.75, 0.05, 0.35],
      ...                 [0.45, 0.75, 0.05],
      ...                 [0.05, 0.55, 0.75],
      ...                 [0.05, 0.65, 0.05]])
      >>> target = tensor([[1, 0, 1],
      ...                  [0, 0, 0],
      ...                  [0, 1, 1],
      ...                  [1, 1, 1]])
      >>> metric = MultilabelPrecisionAtFixedRecall(num_labels=3, min_recall=0.5, thresholds=None)
      >>> metric(preds, target)
      (tensor([1.0000, 0.6667, 1.0000]), tensor([0.7500, 0.5500, 0.3500]))
      >>> mlrafp = MultilabelPrecisionAtFixedRecall(num_labels=3, min_recall=0.5, thresholds=5)
      >>> mlrafp(preds, target)
      (tensor([1.0000, 0.6667, 1.0000]), tensor([0.7500, 0.5000, 0.2500]))
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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 _multilabel_recall_at_fixed_precision_arg_compute(
          state,
          self.num_labels,
          self.thresholds,
          self.ignore_index,
          self.min_recall,
          reduce_fn=_precision_at_recall,
      )
  
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.precision_fixed_recall import _precision_at_recall
  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 MultilabelPrecisionAtFixedRecall obj's underlying member method code with example usage: obj.compute() and returns: _multilabel_recall_at_fixed_precision_arg_compute