class_docstr: Compute `Specificity`_ for binary 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 a score of 0 is returned.
  
  As input to ``forward`` and ``update`` the metric accepts the following input:
  
  - ``preds`` (:class:`~torch.Tensor`): An int or float tensor of shape ``(N, ...)``. 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, ...)``
  
  As output to ``forward`` and ``compute`` the metric returns the following output:
  
  - ``bs`` (:class:`~torch.Tensor`): If ``multidim_average`` is set to ``global``, the metric returns a scalar value.
    If ``multidim_average`` is set to ``samplewise``, the metric returns ``(N,)`` vector consisting of a scalar value
    per sample.
  
  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:
      threshold: Threshold for transforming probability to binary {0,1} predictions
      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 BinarySpecificity
      >>> target = tensor([0, 1, 0, 1, 0, 1])
      >>> preds = tensor([0, 0, 1, 1, 0, 1])
      >>> metric = BinarySpecificity()
      >>> metric(preds, target)
      tensor(0.6667)
  
  Example (preds is float tensor):
      >>> from torchmetrics.classification import BinarySpecificity
      >>> target = tensor([0, 1, 0, 1, 0, 1])
      >>> preds = tensor([0.11, 0.22, 0.84, 0.73, 0.33, 0.92])
      >>> metric = BinarySpecificity()
      >>> metric(preds, target)
      tensor(0.6667)
  
  Example (multidim tensors):
      >>> from torchmetrics.classification import BinarySpecificity
      >>> 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 = BinarySpecificity(multidim_average='samplewise')
      >>> metric(preds, target)
      tensor([0.0000, 0.3333])
docstr: Plot a single or multiple values from the metric.
  
  Args:
      val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
          If no value is provided, will automatically call `metric.compute` and plot that result.
      ax: An matplotlib axis object. If provided will add plot to that axis
  
  Returns:
      Figure object and Axes object
  
  Raises:
      ModuleNotFoundError:
          If `matplotlib` is not installed
  
  .. plot::
      :scale: 75
  
      >>> from torch import rand, randint
      >>> # Example plotting a single value
      >>> from torchmetrics.classification import BinarySpecificity
      >>> metric = BinarySpecificity()
      >>> metric.update(rand(10), randint(2,(10,)))
      >>> fig_, ax_ = metric.plot()
  
  .. plot::
      :scale: 75
  
      >>> from torch import rand, randint
      >>> # Example plotting multiple values
      >>> from torchmetrics.classification import BinarySpecificity
      >>> metric = BinarySpecificity()
      >>> values = [ ]
      >>> for _ in range(10):
      ...     values.append(metric(rand(10), randint(2,(10,))))
      >>> fig_, ax_ = metric.plot(values)
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code: def plot(
      self,
      val: Optional[Union[Tensor, Sequence[Tensor]]] = None,
      ax: Optional[_AX_TYPE] = None,
  ) -> _PLOT_OUT_TYPE:
      """Plot a single or multiple values from the metric.
  
      Args:
          val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
              If no value is provided, will automatically call `metric.compute` and plot that result.
          ax: An matplotlib axis object. If provided will add plot to that axis
  
      Returns:
          Figure object and Axes object
  
      Raises:
          ModuleNotFoundError:
              If `matplotlib` is not installed
  
      .. plot::
          :scale: 75
  
          >>> from torch import rand, randint
          >>> # Example plotting a single value
          >>> from torchmetrics.classification import BinarySpecificity
          >>> metric = BinarySpecificity()
          >>> metric.update(rand(10), randint(2,(10,)))
          >>> fig_, ax_ = metric.plot()
  
      .. plot::
          :scale: 75
  
          >>> from torch import rand, randint
          >>> # Example plotting multiple values
          >>> from torchmetrics.classification import BinarySpecificity
          >>> metric = BinarySpecificity()
          >>> values = [ ]
          >>> for _ in range(10):
          ...     values.append(metric(rand(10), randint(2,(10,))))
          >>> fig_, ax_ = metric.plot(values)
  
      """
      return self._plot(val, ax)
  
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 2 for calling the BinarySpecificity obj's underlying member method code with example usage: obj.plot(val, ax) and returns: self