code: def specicity_at_sensitivity(
      preds: Tensor,
      target: Tensor,
      task: Literal["binary", "multiclass", "multilabel"],
      min_sensitivity: float,
      thresholds: Optional[Union[int, List[float], Tensor]] = None,
      num_classes: Optional[int] = None,
      num_labels: Optional[int] = None,
      ignore_index: Optional[int] = None,
      validate_args: bool = True,
  ) -> Union[
      Tensor,
      Tuple[Tensor, Tensor, Tensor],
      Tuple[List[Tensor], List[Tensor], List[Tensor]],
  ]:
      r"""Compute the highest possible specificity value given the minimum sensitivity thresholds provided.
  
      .. warning::
          This function was deprecated in v1.3.0 of Torchmetrics and will be removed in v2.0.0.
          Use `specificity_at_sensitivity` instead.
  
      """
      warnings.warn(
          "This method has will be removed in 2.0.0. Use `specificity_at_sensitivity` instead.",
          DeprecationWarning,
          stacklevel=1,
      )
      return specificity_at_sensitivity(
          preds=preds,
          target=target,
          task=task,
          min_sensitivity=min_sensitivity,
          thresholds=thresholds,
          num_classes=num_classes,
          num_labels=num_labels,
          ignore_index=ignore_index,
          validate_args=validate_args,
      )
  
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 12 for calling a global function with example usage: specicity_at_sensitivity(preds, target, task, min_sensitivity, thresholds, num_classes, num_labels, ignore_index, validate_args) and returns: specificity_at_sensitivity