code: def test_geometric_mean_score_prediction(average, expected_gmean):
      y_true, y_pred, _ = make_prediction(binary=False)
  
      gmean = geometric_mean_score(y_true, y_pred, average=average)
      assert gmean == pytest.approx(expected_gmean, rel=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 13 for calling a global function with example usage: test_geometric_mean_score_prediction(average, expected_gmean) without return types