code: def specificity_score(
      y_true,
      y_pred,
      *,
      pos_label=1,
      sample_weight=None,
      zero_division="warn",
  ):
      """Compute the specificity or true negative rate.
  
      Args:
          y_true (array-like): Ground truth (correct) target values.
          y_pred (array-like): Estimated targets as returned by a classifier.
          pos_label (scalar, optional): The label of the positive class.
          sample_weight (array-like, optional): Sample weights.
          zero_division ('warn', 0 or 1): Sets the value to return when there is a
              zero division. If set to “warn”, this acts as 0, but warnings are
              also raised.
      """
      _check_zero_division(zero_division)
      MCM = multilabel_confusion_matrix(
          y_true,
          y_pred,
          labels=[pos_label],
          sample_weight=sample_weight,
      )
      tn, fp = MCM[:, 0, 0], MCM[:, 0, 1]
      negs = tn + fp
      return _prf_divide(
          tn,
          negs,
          "specificity",
          "negative",
          None,
          ("specificity",),
          zero_division,
      ).item()
  
imports: from itertools import permutations
  from typing import Union
  import warnings
  import numpy
  import pandas
  from scipy.special import rel_entr
  from sklearn.metrics import make_scorer, recall_score, precision_score
  from sklearn.metrics import multilabel_confusion_matrix
  from sklearn.metrics._classification import _prf_divide, _check_zero_division
  from sklearn.neighbors import NearestNeighbors
  from sklearn.utils import check_X_y
  from aif360.metrics import ot_metric
  from aif360.sklearn.utils import check_inputs, check_groups
  from aif360.detectors.mdss.ScoringFunctions import BerkJones, Bernoulli
  from aif360.detectors.mdss.MDSS import MDSS
label_desc: Use image node_id 8 for calling a global function with example usage: specificity_score(y_true, y_pred) and returns: _prf_divide