code: def _binary_specificity_at_sensitivity_compute(
      state: Union[Tensor, Tuple[Tensor, Tensor]],
      thresholds: Optional[Tensor],
      min_sensitivity: float,
      pos_label: int = 1,
  ) -> Tuple[Tensor, Tensor]:
      fpr, sensitivity, thresholds = _binary_roc_compute(
          state, thresholds, pos_label
      )
      specificity = _convert_fpr_to_specificity(fpr)
      return _specificity_at_sensitivity(
          specificity, sensitivity, thresholds, 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 4 for calling a global function with example usage: _binary_specificity_at_sensitivity_compute(state, thresholds, min_sensitivity, pos_label) and returns: _specificity_at_sensitivity