code: def _calc_specificity(tn, fp):
      """Safely calculate specificity."""
      denom = tn + fp
      if np.isscalar(tn):
          denom = 1.0 if denom == 0 else denom
      else:
          denom[denom == 0] = 1.0
      return tn / denom
  
imports: from functools import partial
  import numpy
  import pytest
  import torch
  from scipy.special import expit
  from sklearn.metrics import confusion_matrix
  from torch import Tensor, tensor
  from torchmetrics.classification.specificity import BinarySpecificity, MulticlassSpecificity, MultilabelSpecificity, Specificity
  from torchmetrics.functional.classification.specificity import binary_specificity, multiclass_specificity, multilabel_specificity
  from torchmetrics.metric import Metric
  from unittests import NUM_CLASSES, THRESHOLD
  from unittests.classification.inputs import _binary_cases, _multiclass_cases, _multilabel_cases
  from unittests.helpers import seed_all
  from unittests.helpers.testers import MetricTester, inject_ignore_index
label_desc: Use image node_id 1 for calling a global function with example usage: _calc_specificity(tn, fp) and returns: unknown