class_docstr: Compute `Specificity`_ for multiclass tasks.
  
  .. math:: \text{Specificity} = \frac{\text{TN}}{\text{TN} + \text{FP}}
  
  Where :math:`\text{TN}` and :math:`\text{FP}` represent the number of true negatives and false positives
  respectively.  The metric is only proper defined when :math:`\text{TN} + \text{FP} \neq 0`. If this case is
  encountered for any class, the metric for that class 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 of shape ``(N, ...)`` or float tensor of shape ``(N, C, ..)``.
    If preds is a floating point we apply ``torch.argmax`` along the ``C`` dimension to automatically convert
    probabilities/logits into an int tensor.
  - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``
  
  As output to ``forward`` and ``compute`` the metric returns the following output:
  
  - ``mcs`` (: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_classes: Integer specifying the number of classes
      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
  
      top_k:
          Number of highest probability or logit score predictions considered to find the correct label.
          Only works when ``preds`` contain probabilities/logits.
      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 MulticlassSpecificity
      >>> target = tensor([2, 1, 0, 0])
      >>> preds = tensor([2, 1, 0, 1])
      >>> metric = MulticlassSpecificity(num_classes=3)
      >>> metric(preds, target)
      tensor(0.8889)
      >>> mcs = MulticlassSpecificity(num_classes=3, average=None)
      >>> mcs(preds, target)
      tensor([1.0000, 0.6667, 1.0000])
  
  Example (preds is float tensor):
      >>> from torchmetrics.classification import MulticlassSpecificity
      >>> target = tensor([2, 1, 0, 0])
      >>> preds = tensor([[0.16, 0.26, 0.58],
      ...                 [0.22, 0.61, 0.17],
      ...                 [0.71, 0.09, 0.20],
      ...                 [0.05, 0.82, 0.13]])
      >>> metric = MulticlassSpecificity(num_classes=3)
      >>> metric(preds, target)
      tensor(0.8889)
      >>> mcs = MulticlassSpecificity(num_classes=3, average=None)
      >>> mcs(preds, target)
      tensor([1.0000, 0.6667, 1.0000])
  
  Example (multidim tensors):
      >>> from torchmetrics.classification import MulticlassSpecificity
      >>> target = tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]])
      >>> preds = tensor([[[0, 2], [2, 0], [0, 1]], [[2, 2], [2, 1], [1, 0]]])
      >>> metric = MulticlassSpecificity(num_classes=3, multidim_average='samplewise')
      >>> metric(preds, target)
      tensor([0.7500, 0.6556])
      >>> mcs = MulticlassSpecificity(num_classes=3, multidim_average='samplewise', average=None)
      >>> mcs(preds, target)
      tensor([[0.7500, 0.7500, 0.7500],
              [0.8000, 0.6667, 0.5000]])
docstr: Compute metric.
docstr_tok: ["Compute","metric","."]
code: def compute(self) -> Tensor:
      """Compute metric."""
      tp, fp, tn, fn = self._final_state()
      return _specificity_reduce(
          tp,
          fp,
          tn,
          fn,
          average=self.average,
          multidim_average=self.multidim_average,
      )
  
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.specificity import _specificity_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 MulticlassSpecificity obj's underlying member method code with example usage: obj.compute() and returns: _specificity_reduce