class_docstr: Compute `Specificity`_.
  
  .. 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{TP} + \text{FP} \neq 0`. If this case is
  encountered for any class/label, the metric for that class/label will be set to 0 and the overall metric may
  therefore be affected in turn.
  
  This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the
  ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``multilabel``. See the documentation of
  :class:`~torchmetrics.classification.BinarySpecificity`, :class:`~torchmetrics.classification.MulticlassSpecificity`
  and :class:`~torchmetrics.classification.MultilabelSpecificity` for the specific details of each argument influence
  and examples.
  
  Legacy Example:
      >>> from torch import tensor
      >>> preds  = tensor([2, 0, 2, 1])
      >>> target = tensor([1, 1, 2, 0])
      >>> specificity = Specificity(task="multiclass", average='macro', num_classes=3)
      >>> specificity(preds, target)
      tensor(0.6111)
      >>> specificity = Specificity(task="multiclass", average='micro', num_classes=3)
      >>> specificity(preds, target)
      tensor(0.6250)
docstr: Initialize task metric.
docstr_tok: ["Initialize","task","metric","."]
code: def __new__(  # type: ignore[misc]
      cls: Type["Specificity"],
      task: Literal["binary", "multiclass", "multilabel"],
      threshold: float = 0.5,
      num_classes: Optional[int] = None,
      num_labels: Optional[int] = None,
      average: Optional[
          Literal["micro", "macro", "weighted", "none"]
      ] = "micro",
      multidim_average: Optional[
          Literal["global", "samplewise"]
      ] = "global",
      top_k: Optional[int] = 1,
      ignore_index: Optional[int] = None,
      validate_args: bool = True,
      **kwargs: Any,
  ) -> Metric:
      """Initialize task metric."""
      task = ClassificationTask.from_str(task)
      assert (
          multidim_average is not None
      )  # noqa: S101  # needed for mypy
      kwargs.update(
          {
              "multidim_average": multidim_average,
              "ignore_index": ignore_index,
              "validate_args": validate_args,
          }
      )
      if task == ClassificationTask.BINARY:
          return BinarySpecificity(threshold, **kwargs)
      if task == ClassificationTask.MULTICLASS:
          if not isinstance(num_classes, int):
              raise ValueError(
                  f"`num_classes` is expected to be `int` but `{type(num_classes)} was passed.`"
              )
          if not isinstance(top_k, int):
              raise ValueError(
                  f"`top_k` is expected to be `int` but `{type(top_k)} was passed.`"
              )
          return MulticlassSpecificity(
              num_classes, top_k, average, **kwargs
          )
      if task == ClassificationTask.MULTILABEL:
          if not isinstance(num_labels, int):
              raise ValueError(
                  f"`num_labels` is expected to be `int` but `{type(num_labels)} was passed.`"
              )
          return MultilabelSpecificity(
              num_labels, threshold, average, **kwargs
          )
      raise ValueError(f"Task {task} not supported!")
  
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 Specificity obj's underlying member method code with example usage: obj.__new__(cls, task, threshold, num_classes, num_labels, average, multidim_average, top_k, ignore_index, validate_args) and returns: BinarySpecificity, MulticlassSpecificity, MultilabelSpecificity