def METHOD_NAME(drifted_data):
    # Arrange
    train_ds, test_ds = drifted_data
    train_ds = Dataset(train_ds.data.drop(columns=['numeric_with_drift', 'categorical_with_drift']),
                       label=train_ds.label_name)
    test_ds = Dataset(test_ds.data.drop(columns=['numeric_with_drift', 'categorical_with_drift']),
                      label=test_ds.label_name)
    check = MultivariateDrift()
    # Act
    result = check.run(train_ds, test_ds)
    # Assert
    assert_that(result.value, has_entries({
        'domain_classifier_auc': close_to(0.5, 0.03),
        'domain_classifier_drift_score': close_to(0, 0.01),
        'domain_classifier_feature_importance': has_entries(
            {'categorical_without_drift': close_to(0.81, 0.001),
             'numeric_without_drift': close_to(0.2, 0.02)}
        ),
    }))