code: def _binary_specificity_at_sensitivity_arg_validation(
      min_sensitivity: float,
      thresholds: Optional[Union[int, List[float], Tensor]] = None,
      ignore_index: Optional[int] = None,
  ) -> None:
      _binary_precision_recall_curve_arg_validation(
          thresholds, ignore_index
      )
      if not isinstance(min_sensitivity, float) and not (
          0 <= min_sensitivity <= 1
      ):
          raise ValueError(
              f"Expected argument `min_sensitivity` to be an float in the [0,1] range, but got {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 3 for calling a global function with example usage: _binary_specificity_at_sensitivity_arg_validation(min_sensitivity, thresholds, ignore_index) without return types