class_docstr: Compute `Precision`_ for multilabel tasks.
  
  .. math:: \text{Precision} = \frac{\text{TP}}{\text{TP} + \text{FP}}
  
  Where :math:`\text{TP}` and :math:`\text{FP}` represent the number of true positives and false positives
  respectively. The metric is only proper defined when :math:`\text{TP} + \text{FP} \neq 0`. If this case is
  encountered for any label, the metric for that label will be set to 0 and the overall metric may therefore be
  affected in turn.
  
  As input to ``forward`` and ``update`` the metric accepts the following input:
  
  - ``preds`` (:class:`~torch.Tensor`): An int tensor or float tensor of shape ``(N, C, ...)``.
    If preds is a floating point tensor with values outside [0,1] range we consider the input to be logits and
    will auto apply sigmoid per element. Additionally, we convert to int tensor with thresholding using the value
    in ``threshold``.
  - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, C, ...)``.
  
  As output to ``forward`` and ``compute`` the metric returns the following output:
  
  - ``mlp`` (:class:`~torch.Tensor`): The returned shape depends on the ``average`` and ``multidim_average``
    arguments:
  
      - If ``multidim_average`` is set to ``global``:
  
        - If ``average='micro'/'macro'/'weighted'``, the output will be a scalar tensor
        - If ``average=None/'none'``, the shape will be ``(C,)``
  
      - If ``multidim_average`` is set to ``samplewise``:
  
        - If ``average='micro'/'macro'/'weighted'``, the shape will be ``(N,)``
        - If ``average=None/'none'``, the shape will be ``(N, C)``
  
  If ``multidim_average`` is set to ``samplewise`` we expect at least one additional dimension ``...`` to be present,
  which the reduction will then be applied over instead of the sample dimension ``N``.
  
  Args:
      num_labels: Integer specifying the number of labels
      threshold: Threshold for transforming probability to binary (0,1) predictions
      average:
          Defines the reduction that is applied over labels. Should be one of the following:
  
          - ``micro``: Sum statistics over all labels
          - ``macro``: Calculate statistics for each label and average them
          - ``weighted``: calculates statistics for each label and computes weighted average using their support
          - ``"none"`` or ``None``: calculates statistic for each label and applies no reduction
  
      multidim_average:
          Defines how additionally dimensions ``...`` should be handled. Should be one of the following:
  
          - ``global``: Additional dimensions are flatted along the batch dimension
          - ``samplewise``: Statistic will be calculated independently for each sample on the ``N`` axis.
            The statistics in this case are calculated over the additional dimensions.
  
      ignore_index:
          Specifies a target value that is ignored and does not contribute to the metric calculation
      validate_args: bool indicating if input arguments and tensors should be validated for correctness.
          Set to ``False`` for faster computations.
  
  Example (preds is int tensor):
      >>> from torch import tensor
      >>> from torchmetrics.classification import MultilabelPrecision
      >>> target = tensor([[0, 1, 0], [1, 0, 1]])
      >>> preds = tensor([[0, 0, 1], [1, 0, 1]])
      >>> metric = MultilabelPrecision(num_labels=3)
      >>> metric(preds, target)
      tensor(0.5000)
      >>> mlp = MultilabelPrecision(num_labels=3, average=None)
      >>> mlp(preds, target)
      tensor([1.0000, 0.0000, 0.5000])
  
  Example (preds is float tensor):
      >>> from torchmetrics.classification import MultilabelPrecision
      >>> target = tensor([[0, 1, 0], [1, 0, 1]])
      >>> preds = tensor([[0.11, 0.22, 0.84], [0.73, 0.33, 0.92]])
      >>> metric = MultilabelPrecision(num_labels=3)
      >>> metric(preds, target)
      tensor(0.5000)
      >>> mlp = MultilabelPrecision(num_labels=3, average=None)
      >>> mlp(preds, target)
      tensor([1.0000, 0.0000, 0.5000])
  
  Example (multidim tensors):
      >>> from torchmetrics.classification import MultilabelPrecision
      >>> target = tensor([[[0, 1], [1, 0], [0, 1]], [[1, 1], [0, 0], [1, 0]]])
      >>> preds = tensor([[[0.59, 0.91], [0.91, 0.99],  [0.63, 0.04]],
      ...                 [[0.38, 0.04], [0.86, 0.780], [0.45, 0.37]]])
      >>> metric = MultilabelPrecision(num_labels=3, multidim_average='samplewise')
      >>> metric(preds, target)
      tensor([0.3333, 0.0000])
      >>> mlp = MultilabelPrecision(num_labels=3, multidim_average='samplewise', average=None)
      >>> mlp(preds, target)
      tensor([[0.5000, 0.5000, 0.0000],
              [0.0000, 0.0000, 0.0000]])
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docstr: Compute metric.
docstr_tok: ["Compute","metric","."]
code: def compute(self) -> Tensor:
      """Compute metric."""
      tp, fp, tn, fn = self._final_state()
      return _precision_recall_reduce(
          "precision",
          tp,
          fp,
          tn,
          fn,
          average=self.average,
          multidim_average=self.multidim_average,
          multilabel=True,
      )
  
imports: from typing import Any, Optional, Sequence, Type, Union
  from torch import Tensor
  from typing_extensions import Literal
  from torchmetrics.classification.base import _ClassificationTaskWrapper
  from torchmetrics.classification.stat_scores import BinaryStatScores, MulticlassStatScores, MultilabelStatScores
  from torchmetrics.functional.classification.precision_recall import _precision_recall_reduce
  from torchmetrics.metric import Metric
  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 1 for calling the MultilabelPrecision obj's underlying member method code with example usage: obj.compute() and returns: _precision_recall_reduce