def METHOD_NAME(system = system(), data_set = 'train',
                    num_classes = 1000):
    """ImageNet label file on LC system.
    The file contains ground truth labels from the ILSVRC2012
    competition. It is a plain text file where each line contains an
    image file path (relative to the ImageNet directory; see the
    `imagenet_dir` function) and the corresponding label ID.
    There are three available data sets: 'training', 'validation', and
    'testing'.
    Some of these data sets have been preprocessed to only include
    images in a subset of the label classes, e.g. images in the first
    10 label classes. This is convenient for quickly evaluating
    performance or learning behavior. The availabiilty of these
    subsampled data sets may vary by system.
    """
    label_dir = parallel_file_system_path(system)
    if system in ('lassen', 'sierra'):
        label_dir += 'lbann/datasets/ILSVRC2012/labels/'
    else:
        label_dir += 'lbann/datasets/ILSVRC2012/labels/'
    suffixes = {1000: '', 10: '_c0-9', 100: '_c0-99',
                200: '_c100-299', 300: '_c0-299'}
    if data_set.lower() in ('train', 'training'):
        if num_classes in suffixes.keys():
            return os.path.join(label_dir,
                                'train' + suffixes[num_classes] + '.txt')
        else:
            raise RuntimeError('invalid number of classes ({0}) '
                               'for ImageNet data set ({1})'
                               .format(num_classes, data_set))
    elif data_set.lower() in ('val', 'validation'):
        if num_classes in suffixes.keys():
            return os.path.join(label_dir,
                                'val' + suffixes[num_classes] + '.txt')
        else:
            raise RuntimeError('invalid number of classes ({0}) '
                               'for ImageNet data set ({1})'
                               .format(num_classes, data_set))
    elif data_set.lower() in ('test', 'testing'):
        return os.path.join(label_dir, 'test.txt')
    else:
        raise RuntimeError('unknown ImageNet data set (' + data_set + ')')