code: def test_sensitivity_specificity_unused_pos_label():
      # but average != 'binary'; even if data is binary
      msg = r"use labels=\[pos_label\] to specify a single"
      with pytest.warns(UserWarning, match=msg):
          sensitivity_specificity_support(
              [1, 2, 1], [1, 2, 2], pos_label=2, average="macro"
          )
  
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 8 for calling a global function with example usage: test_sensitivity_specificity_unused_pos_label() without return types