class_docstr: Compute the highest possible specificity value given the minimum sensitivity thresholds provided.
  
  This is done by first calculating the Receiver Operating Characteristic (ROC) curve for different thresholds and the
  find the specificity for a given sensitivity level.
  
  Accepts the following input tensors:
  
  - ``preds`` (float tensor): ``(N, ...)``. 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`` (int tensor): ``(N, ...)``. Target should be a tensor containing ground truth labels, and therefore
    only contain {0,1} values (except if `ignore_index` is specified).
  
  Additional dimension ``...`` will be flattened into the batch dimension.
  
  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})` (constant memory).
  
  Args:
      min_sensitivity: float value specifying minimum sensitivity 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 `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.
  
  Returns:
      (tuple): a tuple of 2 tensors containing:
  
      - specificity: an scalar tensor with the maximum specificity for the given sensitivity level
      - threshold: an scalar tensor with the corresponding threshold level
  
  Example:
      >>> from torchmetrics.classification import BinarySpecificityAtSensitivity
      >>> from torch import tensor
      >>> preds = tensor([0, 0.5, 0.4, 0.1])
      >>> target = tensor([0, 1, 1, 1])
      >>> metric = BinarySpecificityAtSensitivity(min_sensitivity=0.5, thresholds=None)
      >>> metric(preds, target)
      (tensor(1.), tensor(0.4000))
      >>> metric = BinarySpecificityAtSensitivity(min_sensitivity=0.5, thresholds=5)
      >>> metric(preds, target)
      (tensor(1.), tensor(0.2500))
docstr: Compute metric.
docstr_tok: ["Compute","metric","."]
code: def compute(self) -> Tuple[Tensor, Tensor]:  # type: ignore[override]
      """Compute metric."""
      state = (
          (_cat(self.preds), _cat(self.target))
          if self.thresholds is None
          else self.confmat
      )
      return _binary_specificity_at_sensitivity_compute(
          state, self.thresholds, self.min_sensitivity
      )
  
imports: from typing import Any, List, Optional, 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.specificity_sensitivity import _binary_specificity_at_sensitivity_arg_validation, _binary_specificity_at_sensitivity_compute, _multiclass_specificity_at_sensitivity_arg_validation, _multiclass_specificity_at_sensitivity_compute, _multilabel_specificity_at_sensitivity_arg_validation, _multilabel_specificity_at_sensitivity_compute
  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
label_desc: Use image node_id 2 for calling the BinarySpecificityAtSensitivity obj's underlying member method code with example usage: obj.compute() and returns: _binary_specificity_at_sensitivity_compute