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().add_condition_overall_drift_value_less_than()
    # Act
    result = check.run(train_ds, test_ds)
    condition_result, *_ = check.conditions_decision(result)
    # Assert
    assert_that(condition_result, equal_condition_result(
        is_pass=True,
        details='Found drift value of: 0, corresponding to a domain classifier AUC of: 0.5',
        name='Drift value is less than 0.25',
    ))