How to use the snakemake.utils.update_config function in snakemake

def update_config(config):
    """
    Populates config file with default config values.
    And made changes if necessary.

    """

    # in old version java_mem was used, new is mem
    if ('java_mem' in config) and (not ('mem' in config)):
        config['mem']=config['java_mem']


    # get default values and update them with values specified in config file
    default_config = make_default_config()
    utils.update_config(default_config, config)

    return default_config

ap = argparse.ArgumentParser()
ap.add_argument('config', help='Main config.yaml file')
ap.add_argument('hub_config', help='Track hub config YAML file')
ap.add_argument('--additional-configs', nargs='+',
                help='Additional config files with which to update the main '
                'config')
args = ap.parse_args()

# Access configured options. See comments in example hub_config.yaml for
# details
config = yaml.load(open(args.config), Loader=yaml.FullLoader)

if args.additional_configs:
    for cfg in args.additional_configs:
        update_config(config, yaml.load(open(cfg), Loader=yaml.FullLoader))

hub_config = yaml.load(open(args.hub_config), Loader=yaml.FullLoader)

hub, genomes_file, genome, trackdb = default_hub(
    hub_name=hub_config['hub']['name'],
    short_label=hub_config['hub']['short_label'],
    long_label=hub_config['hub']['long_label'],
    email=hub_config['hub']['email'],
    genome=hub_config['hub']['genome']
)

c = ChIPSeqConfig(config, os.path.join(os.path.dirname(args.config), 'chipseq_patterns.yaml'))

# Set up subgroups based on unique values from columns specified in the config
df = pandas.read_csv(config['sampletable'], comment='#', sep='\t')
cols = hub_config['subgroups']['columns']

try:
        with open(metadata_file, "r") as fh:
            reader = csv.DictReader(fh.readlines())
        metadata_list = [row for row in reader]
        run2sample = {row["Run"]:row["SampleName"] for row in metadata_list}
        config_default = {
            'bio.ngs.settings' : {
                'sampleinfo' : metadata_file
            },
            'bio.ngs.tools.sratools': {
                '_datadir': os.path.dirname(metadata_file),
                '_run2sample' : run2sample,
                '_metadata' : metadata_list
            },
        }
        update_config(config_default, config)
        config = config_default
        
    except Exception:
        raise Exception("""

        no metadata file '{metadata}' found

        please initiate analysis by running 'snakemake {metadata}'

        """.format(metadata=metadata_file))
    return config

ap = argparse.ArgumentParser()
ap.add_argument('config', help='Main config.yaml file')
ap.add_argument('hub_config', help='Track hub config YAML file')
ap.add_argument('--additional-configs', nargs='+',
                help='Additional config files with which to update the main '
                'config',)
args = ap.parse_args()

# Access configured options. See comments in example hub_config.yaml for
# details
config = yaml.load(open(args.config), Loader=yaml.FullLoader)
hub_config = yaml.load(open(args.hub_config), Loader=yaml.FullLoader)

if args.additional_configs:
    for cfg in args.additional_configs:
        update_config(config, yaml.load(open(cfg), Loader=yaml.FullLoader))

c = RNASeqConfig(config, os.path.join(os.path.dirname(args.config), 'rnaseq_patterns.yaml'))

hub, genomes_file, genome, trackdb = default_hub(
    hub_name=hub_config['hub']['name'],
    short_label=hub_config['hub']['short_label'],
    long_label=hub_config['hub']['long_label'],
    email=hub_config['hub']['email'],
    genome=hub_config['hub']['genome']
)

# Set up subgroups based on the configured columns
df = pandas.read_csv(config['sampletable'], comment='#', sep='\t')
cols = hub_config['subgroups']['columns']
subgroups = []
for col in cols: