code: def test_sensitivity_specificity_extra_labels(
      average, expected_specificty
  ):
      y_true = [1, 3, 3, 2]
      y_pred = [1, 1, 3, 2]
  
      actual = specificity_score(
          y_true, y_pred, labels=[0, 1, 2, 3, 4], average=average
      )
      assert_allclose(expected_specificty, actual, rtol=R_TOL)
  
imports: from functools import partial
  import numpy
  import pytest
  from sklearn import datasets, svm
  from sklearn.metrics import accuracy_score, average_precision_score, brier_score_loss, cohen_kappa_score, jaccard_score, precision_score, recall_score, roc_auc_score
  from sklearn.preprocessing import label_binarize
  from sklearn.utils._testing import assert_allclose, assert_array_equal, assert_no_warnings
  from sklearn.utils.validation import check_random_state
  from imblearn.metrics import classification_report_imbalanced, geometric_mean_score, macro_averaged_mean_absolute_error, make_index_balanced_accuracy, sensitivity_score, sensitivity_specificity_support, specificity_score
label_desc: Use image node_id 4 for calling a global function with example usage: test_sensitivity_specificity_extra_labels(average, expected_specificty) without return types