How to use the deephyper.benchmarks_hps.cliparser.build_base_parser function in deephyper

def build_parser():
    # Build this benchmark"s cli parser on top of the keras_cli parser.
    parser = build_base_parser()

    # Benchmark specific hyperparameters.
    parser.add_argument("--embedding_dims", action="store", dest="embedding_dims",
                        nargs="?", const=2, type = int, default = 50,
                        help="how many embedding dims will be added to the model")

    parser.add_argument("--filters", action="store", dest="filters",
                        nargs="?", const=2, type = int, default = 250,
                        help="the number of output filters in the convolution")

    parser.add_argument("--hidden_dims", action="store", dest="hidden_dims",
                        nargs="?", const=2, type = int, default = 250,
                        help="hidden dims of a vanilla hidden layer")

    parser.add_argument("--kernel_size", action="store", dest="kernel_size",
                        nargs="?", const=2, type = int, default = 3,

def build_parser():
    # Build this benchmark's cli parser on top of the base parser.
    parser = build_base_parser()

    # Benchmark specific hyperparameters.
    parser.add_argument("--base_lr", action="store", dest="base_lr",
                        nargs="?", const=2, type=int, default=1e-3,)

    parser.add_argument("--lr80", action="store", dest="lr80",
                        nargs="?", const=2, type=int, default=1e-1)

    parser.add_argument("--lr120", action="store", dest="lr120",
                        nargs="?", const=2, type=int, default=1e-2)

    parser.add_argument("--lr160", action="store", dest="lr160",
                        nargs="?", const=2, type=int, default=1e-3)

    parser.add_argument("--lr180", action="store", dest="lr180",
                        nargs="?", const=2, type=int, default=0.5e-3)

def build_parser():
    # Build this benchmark"s cli parser on top of the base parser.
    parser = build_base_parser()

    # Benchmark specific hyperparameters.
    parser.add_argument("--f1_size", action="store", dest="f1_size",
                        nargs="?", const=2, type=int, default=3,
                        help="Filter 1 dim")

    parser.add_argument("--f2_size", action="store", dest="f2_size",
                        nargs="?", const=2, type=int, default=3,
                        help="Filter 2 dim")

    parser.add_argument("--f1_units", action="store", dest="f1_units",
                        nargs="?", const=2, type=int, default=32,
                        help="Filter 1 units")

    parser.add_argument("--f2_units", action="store", dest="f2_units",
                        nargs="?", const=2, type=int, default=64,

def build_parser():
    parser = build_base_parser()
    return parser

def build_parser():
    # Build this benchmark"s cli parser on top of the keras_cli parser.
    parser = build_base_parser()

    # Benchmark specific hyperparameters.
    parser.add_argument("--nunits", action="store", dest="nunits",
                        nargs="?", const=1, type=int, default=512,
                        help="Dense units")

    parser.add_argument("--max_words", action="store", dest="max_words",
                        nargs="?", const=2, type=int, default=1000)

    parser.add_argument("--skip_top", action="store", dest="skip_top",
                        nargs="?", const=2, type=int, default=0)

    return parser

def build_parser():
    parser = build_base_parser()

    parser.add_argument('--max_features', action='store', dest='max_features',
                        nargs='?', const=2, type = int, default='20000',
                        help='max_features when loading data')

    parser.add_argument('--maxlen', action='store', dest='maxlen',
                        nargs='?', const=2, type = int, default='400',
                        help='the max length of the sequence of x_train and x_test')
                            
    parser.add_argument('--embedding_dims', action='store', dest='embedding_dims',
                        nargs='?', const=2, type = int, default = '50',
                        help='how many embedding dims will be added to the model')
    return parser

def build_parser():
    # Build this benchmark"s cli parser on top of the keras_cli parser.
    parser = build_base_parser()

    # Benchmark specific hyperparameters.
    parser.add_argument("--units", action="store", dest="units",
                        nargs="?", const=1, type=int, default=64,
                        help="units for LSTM")

    parser.add_argument("--max_features", action="store", dest="max_features",
                        nargs="?", const=2, type = int, default=20000,
                        help="max_features when loading data")

    parser.add_argument("--maxlen", action="store", dest="maxlen",
                        nargs="?", const=2, type = int, default=100,
                        help="the max length of the sequence of x_train and x_test")

    parser.add_argument("--embedding_dims", action="store", dest="embedding_dims",
                        nargs="?", const=2, type = int, default = 128,

def build_parser():
    # Build this benchmark"s cli parser on top of the keras_cli parser.
    parser = build_base_parser()

    # Benchmark specific hyperparameters.

    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