code: def test_sensitivity_specificity_f_binary_single_class(
      y_pred, expected_sensitivity, expected_specificity
  ):
      # Such a case may occur with non-stratified cross-validation
      assert sensitivity_score(*y_pred) == expected_sensitivity
      assert specificity_score(*y_pred) == expected_specificity
  
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 3 for calling a global function with example usage: test_sensitivity_specificity_f_binary_single_class(y_pred, expected_sensitivity, expected_specificity) without return types