code: def multilabel_specificity_at_sensitivity(
      preds: Tensor,
      target: Tensor,
      num_labels: int,
      min_sensitivity: float,
      thresholds: Optional[Union[int, List[float], Tensor]] = None,
      ignore_index: Optional[int] = None,
      validate_args: bool = True,
  ) -> Tuple[Tensor, Tensor]:
      r"""Compute the highest possible specificity value given minimum sensitivity level provided for multilabel tasks.
  
      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, C, ...)``. 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, C, ...)``. 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} \times n_{labels})` (constant memory).
  
      Args:
          preds: Tensor with predictions
          target: Tensor with true labels
          num_labels: Integer specifying the number of labels
          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.
  
          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.
  
      Returns:
          (tuple): a tuple of either 2 tensors or 2 lists containing
  
          - specificity: an 1d tensor of size (n_classes, ) with the maximum recall for the given precision
              level per class
          - thresholds: an 1d tensor of size (n_classes, ) with the corresponding threshold level per class
  
      Example:
          >>> from torchmetrics.functional.classification import multilabel_specificity_at_sensitivity
          >>> preds = torch.tensor([[0.75, 0.05, 0.35],
          ...                       [0.45, 0.75, 0.05],
          ...                       [0.05, 0.55, 0.75],
          ...                       [0.05, 0.65, 0.05]])
          >>> target = torch.tensor([[1, 0, 1],
          ...                        [0, 0, 0],
          ...                        [0, 1, 1],
          ...                        [1, 1, 1]])
          >>> multilabel_specificity_at_sensitivity(preds, target, num_labels=3, min_sensitivity=0.5, thresholds=None)
          (tensor([1.0000, 0.5000, 1.0000]), tensor([0.7500, 0.6500, 0.3500]))
          >>> multilabel_specificity_at_sensitivity(preds, target, num_labels=3, min_sensitivity=0.5, thresholds=5)
          (tensor([1.0000, 0.5000, 1.0000]), tensor([0.7500, 0.5000, 0.2500]))
  
      """
      if validate_args:
          _multilabel_specificity_at_sensitivity_arg_validation(
              num_labels, min_sensitivity, thresholds, ignore_index
          )
          _multilabel_precision_recall_curve_tensor_validation(
              preds, target, num_labels, ignore_index
          )
      (
          preds,
          target,
          thresholds,
      ) = _multilabel_precision_recall_curve_format(
          preds, target, num_labels, thresholds, ignore_index
      )
      state = _multilabel_precision_recall_curve_update(
          preds, target, num_labels, thresholds
      )
      return _multilabel_specificity_at_sensitivity_compute(
          state, num_labels, thresholds, ignore_index, min_sensitivity
      )
  
imports: import warnings
  from typing import List, Optional, Tuple, Union
  import torch
  from torch import Tensor
  from typing_extensions import Literal
  from torchmetrics.functional.classification.precision_recall_curve import _binary_precision_recall_curve_arg_validation, _binary_precision_recall_curve_format, _binary_precision_recall_curve_tensor_validation, _binary_precision_recall_curve_update, _multiclass_precision_recall_curve_arg_validation, _multiclass_precision_recall_curve_format, _multiclass_precision_recall_curve_tensor_validation, _multiclass_precision_recall_curve_update, _multilabel_precision_recall_curve_arg_validation, _multilabel_precision_recall_curve_format, _multilabel_precision_recall_curve_tensor_validation, _multilabel_precision_recall_curve_update
  from torchmetrics.functional.classification.roc import _binary_roc_compute, _multiclass_roc_compute, _multilabel_roc_compute
  from torchmetrics.utilities.enums import ClassificationTask
label_desc: Use image node_id 11 for calling a global function with example usage: multilabel_specificity_at_sensitivity(preds, target, num_labels, min_sensitivity, thresholds, ignore_index, validate_args) and returns: _multilabel_specificity_at_sensitivity_compute