How to use the deephyper.benchmarks.keras_cmdline.create_parser function in deephyper

def augment_parser(parser):

    parser.add_argument('--nunits', action='store', dest='nunits',
                        nargs='?', const=2, type=int, default='512',
                        help='number of units/layer in MLP')

    parser.add_argument('--nhidden', action='store', dest='nhidden',
                        nargs='?', const=2, type=int, default='2',
                        help='number of hidden layers in MLP')

    return parser

if __name__ == "__main__":
    parser = keras_cmdline.create_parser()
    parser = augment_parser(parser)
    cmdline_args = parser.parse_args()
    param_dict = vars(cmdline_args)
    run(param_dict)

nargs='?', const=2, type=int, default='16',
                        help='dimension of capsule')

    parser.add_argument('--routings', action='store', dest='routings',
                        nargs='?', const=2, type=int, default='3',
                        help='dimension of capsule')

    parser.add_argument('--share_weights', action='store', dest='share_weights',
                        nargs='?', const=1, type=util.str2bool, default=True,
                        help='boolean. share weights?')


    return parser

if __name__ == "__main__":
    parser = keras_cmdline.create_parser()
    parser = augment_parser(parser)
    cmdline_args = parser.parse_args()
    param_dict = vars(cmdline_args)
    run(param_dict)

def augment_parser(parser):
    parser.add_argument('--rnn_type', action='store',
                        dest='rnn_type',
                        nargs='?', const=1, type=str, default='LSTM',
                        choices=['LSTM', 'GRU', 'SimpleRNN'],
                        help='type of RNN')

    parser.add_argument('--nhidden', action='store', dest='nhidden',
                        nargs='?', const=2, type=int, default='128',
                        help='number of epochs')
    return parser


if __name__ == "__main__":
    parser = keras_cmdline.create_parser()
    parser = augment_parser(parser)
    cmdline_args = parser.parse_args()
    param_dict = vars(cmdline_args)
    run(param_dict)

help='Filter 2 units')

    parser.add_argument('--p_size', action='store', dest='p_size',
                        nargs='?', const=2, type=int, default='2',
                        help='pool size')

    parser.add_argument('--nunits', action='store', dest='nunits',
                        nargs='?', const=2, type=int, default='512',
                        help='number of units in FC layer')
    parser.add_argument('--dropout2', type=float, default=0.5, 
                        help='dropout after FC layer')

    return parser

if __name__ == "__main__":
    parser = keras_cmdline.create_parser()
    parser = augment_parser(parser)
    cmdline_args = parser.parse_args()
    param_dict = vars(cmdline_args)
    run(param_dict)

def defaults():
    def_parser = keras_cmdline.create_parser()
    def_parser = augment_parser(def_parser)
    return vars(def_parser.parse_args(''))

parser.add_argument('--hidden_size', action='store', dest='hidden_size',
                        nargs='?', const=2, type=int, default='1',
                        help='number of hidden layers')
    parser.add_argument('--nunits', action='store', dest='nunits',
                        nargs='?', const=2, type=int, default='5',
                        help='number of units per hidden layer')
    return parser

def defaults():
    def_parser = keras_cmdline.create_parser()
    def_parser = augment_parser(def_parser)
    return vars(def_parser.parse_args(''))


if __name__ == "__main__":
    parser = keras_cmdline.create_parser()
    parser = augment_parser(parser)
    cmdline_args = parser.parse_args()
    param_dict = vars(cmdline_args)
    run(param_dict)

parser.add_argument('--embed_hidden_size', action='store', dest='embed_hidden_size',
                        nargs='?', const=2, type=int, default='50',
                        help='number of epochs')

    parser.add_argument('--sent_hidden_size', action='store', dest='sent_hidden_size',
                        nargs='?', const=2, type=int, default='100',
                        help='number of epochs')

    parser.add_argument('--query_hidden_size', action='store', dest='query_hidden_size',
                        nargs='?', const=2, type=int, default='100',
                        help='number of epochs')                        

    return parser

if __name__ == "__main__":
    parser = keras_cmdline.create_parser()
    parser = augment_parser(parser)
    cmdline_args = parser.parse_args()
    param_dict = vars(cmdline_args)
    run(param_dict)

#def augment_parser(parser):
#    parser.add_argument("--dense", nargs='+', type=int, default=[2000,600])
#    parser.add_argument('--minval_uniform', type=float, default=-0.05)
#    parser.add_argument('--maxval_uniform', type=float, default=0.05)
#    parser.add_argument('--mean_normal', type=float, default=0.0)
#    parser.add_argument('--stddev_normal', type=float, default=0.05)
#    parser.add_argument("--initialization",
#                        default='glorot_uniform',
#                        choices=['constant', 'uniform', 'normal', 'glorot_uniform', 'lecun_uniform', 'lecun_normal', 'he_normal'])
#    parser.add_argument("--alpha_dropout", action='store_true',
#                        help="use AlphaDropout instead of regular Dropout")
#                        
#    return parser

if __name__ == '__main__':
    parser = keras_cmdline.create_parser()
    #parser = augment_parser(parser)
    cmdline_args = parser.parse_args()
    param_dict = vars(cmdline_args)

    params_p1b1 = initialize_parameters()
    param_dict.update(params_p1b1)
    param_dict['drop'] = param_dict['dropout']
    param_dict['learning_rate'] = param_dict['lr']

    run(param_dict)