code: def test_roc_auc_score_with_sklearn():
      """
      Ensure AutoGluon's custom fast roc_auc_score produces the same result as sklearn's roc_auc_score.
      """
      y_true = np.array([0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1])
      y_score = np.array(
          [0, 1, 0, 1, 0.1, 0.81, 0.76, 0.1, 0.31, 0.32, 0.34, 0.9]
      )
      expected_score = sklearn.metrics.roc_auc_score(y_true, y_score)
      actual_score = roc_auc(y_true, y_score)
  
      assert np.isclose(actual_score, expected_score)
  
imports: import numpy
  import pytest
  import sklearn
  from autogluon.core.metrics import confusion_matrix, log_loss, quadratic_kappa, roc_auc
  from autogluon.core.metrics.softclass_metrics import soft_log_loss
label_desc: Use image node_id 15 for calling a global function with example usage: test_roc_auc_score_with_sklearn() without return types