def METHOD_NAME(parser):
    data = parser.add_argument_group('Data', 'the input images')
    data.add_argument('--data-train', type=str, help='the training data')
    data.add_argument('--data-train-idx', type=str, default='', help='the index of training data')
    data.add_argument('--data-val', type=str, help='the validation data')
    data.add_argument('--data-val-idx', type=str, default='', help='the index of validation data')
    data.add_argument('--rgb-mean', type=str, default='123.68,116.779,103.939',
                      help='a tuple of size 3 for the mean rgb')
    data.add_argument('--pad-size', type=int, default=0,
                      help='padding the input image')
    data.add_argument('--image-shape', type=str,
                      help='the image shape feed into the network, e.g. (3,224,224)')
    data.add_argument('--num-classes', type=int, help='the number of classes')
    data.add_argument('--num-examples', type=int, help='the number of training examples')
    data.add_argument('--data-nthreads', type=int, default=4,
                      help='number of threads for data decoding')
    data.add_argument('--benchmark', type=int, default=0,
                      help='if 1, then feed the network with synthetic data')
    return data