def METHOD_NAME(mnist_visiondata_test):
    check = SingleDatasetPerformance().add_condition_greater_than(0.8) \
        .add_condition_greater_than(1.0, ['Precision']) \
        .add_condition_greater_than(0.5, ['Accuracy']) \
        .add_condition_greater_than(0.5, class_mode='a')
    # Act
    result = check.run(mnist_visiondata_test)
    # Assert
    assert_that(result.conditions_results[0], equal_condition_result(
        is_pass=True,
        name='Score is greater than 0.8 for classes: all',
        details='Passed for all of the metrics.'
    ))
    assert_that(result.conditions_results[1], equal_condition_result(
        is_pass=False,
        name='Score is greater than 1.0 for classes: all',
        details='Failed for metrics: [\'Precision\']'
    ))
    assert_that(result.conditions_results[2], equal_condition_result(
        is_pass=False,
        category=ConditionCategory.ERROR,
        details='Exception in condition: DeepchecksValueError: The requested metric was not calculated, the metrics '
                'calculated in this check are: [\'Precision\' \'Recall\'].',
        name='Score is greater than 0.5 for classes: all'
    ))
    assert_that(result.conditions_results[3], equal_condition_result(
        is_pass=False,
        category=ConditionCategory.ERROR,
        details='Exception in condition: DeepchecksValueError: class_mode expected be one of the classes in the check '
                'results or any or all, received a.',
        name='Score is greater than 0.5 for classes: a'
    ))