How to use the template.set_template function in template

parser.add_argument('--save_results', action='store_true',
                    help='save output results')

# options for residual group and feature channel reduction
parser.add_argument('--n_resgroups', type=int, default=20,
                    help='number of residual groups')
parser.add_argument('--reduction', type=int, default=16,
                    help='number of feature maps reduction')
# options for test
parser.add_argument('--testpath', type=str, default='../test/DIV2K_val_LR_our',
                    help='dataset directory for testing')
parser.add_argument('--testset', type=str, default='Set5',
                    help='dataset name for testing')

args = parser.parse_args()
template.set_template(args)

args.scale = list(map(lambda x: int(x), args.scale.split('+')))

if args.epochs == 0:
    args.epochs = 1e8

for arg in vars(args):
    if vars(args)[arg] == 'True':
        vars(args)[arg] = True
    elif vars(args)[arg] == 'False':
        vars(args)[arg] = False

parser.add_argument('--save_results', action='store_true',
                    help='save output results')

# options for residual group and feature channel reduction
parser.add_argument('--n_resgroups', type=int, default=10,
                    help='number of residual groups')
parser.add_argument('--reduction', type=int, default=16,
                    help='number of feature maps reduction')
# options for test
parser.add_argument('--testpath', type=str, default='../test/DIV2K_val_LR_our',
                    help='dataset directory for testing')
parser.add_argument('--testset', type=str, default='Set5',
                    help='dataset name for testing')

args = parser.parse_args()
template.set_template(args)

args.scale = list(map(lambda x: int(x), args.scale.split('+')))

if args.epochs == 0:
    args.epochs = 1e8

for arg in vars(args):
    if vars(args)[arg] == 'True':
        vars(args)[arg] = True
    elif vars(args)[arg] == 'False':
        vars(args)[arg] = False

help='resume from specific checkpoint')
parser.add_argument('--print_model', action='store_true',
                    help='print model')
parser.add_argument('--save_models', action='store_true',
                    help='save all intermediate models')
parser.add_argument('--print_every', type=int, default=100,
                    help='how many batches to wait before logging training status')
parser.add_argument('--save_results', action='store_true',
                    help='save output results')
parser.add_argument('--save_branches', action='store_true',
                    help='save outputs of each branches in IRL setup')
parser.add_argument('--save_residuals', action='store_true',
                    help='save residuals of output results')

args = parser.parse_args()
template.set_template(args)

args.scale = list(map(lambda x: int(x), args.scale.split('+')))

if args.epochs == 0:
    args.epochs = 1e8

for arg in vars(args):
    if vars(args)[arg] == 'True':
        vars(args)[arg] = True
    elif vars(args)[arg] == 'False':
        vars(args)[arg] = False

# Log specifications
parser.add_argument('--save', type=str, default='test',
                    help='file name to save')
parser.add_argument('--load', type=str, default='.',
                    help='file name to load')
parser.add_argument('--resume', type=int, default=0,
                    help='resume from specific checkpoint')
parser.add_argument('--save_models', action='store_true',
                    help='save all intermediate models')
parser.add_argument('--print_every', type=int, default=100,
                    help='how many batches to wait before logging training status')
parser.add_argument('--save_results', action='store_true',
                    help='save output results')

args = parser.parse_args()
template.set_template(args)

args.scale = list(map(lambda x: int(x), args.scale.split('+')))

if args.epochs == 0:
    args.epochs = 1e8

for arg in vars(args):
    if vars(args)[arg] == 'True':
        vars(args)[arg] = True
    elif vars(args)[arg] == 'False':
        vars(args)[arg] = False

# Log specifications
parser.add_argument('--save', type=str, default='meta',
                    help='file name to save')
parser.add_argument('--load', type=str, default='.',
                    help='file name to load')
parser.add_argument('--resume', type=int, default=0,
                    help='resume from specific checkpoint')
parser.add_argument('--save_models', action='store_true',
                    help='save all intermediate models')
parser.add_argument('--print_every', type=int, default=100,
                    help='how many batches to wait before logging training status')
parser.add_argument('--save_results', action='store_true',
                    help='save output results')

args = parser.parse_args()
template.set_template(args)

#args.scale = list(map(lambda x: int(x), args.scale.split('+')))
###here we redefine the scale

if args.scale=='':
    import numpy as np
    #args.scale = np.linspace(1.1,4,30)
    args.scale = [1.1,1.2,1.3,1.4,1.5,1.6,1.7,1.8,1.9,2.0,2.1,2.2,2.3,2.4,2.5,2.6,2.7,2.8,2.9,3.0,3.1,3.2,3.3,3.4,3.5,3.6,3.7,3.8,3.9,4.0]
    #print(args.scale)
else:
    args.scale = list(map(lambda x: float(x), args.scale.split('+')))
print(args.scale)
if args.epochs == 0:
    args.epochs = 1e8

for arg in vars(args):

help='file name to save')
parser.add_argument('--load', type=str, default='',
                    help='file name to load')
parser.add_argument('--resume', type=int, default=0,
                    help='resume from specific checkpoint')
parser.add_argument('--save_models', action='store_true',
                    help='save all intermediate models')
parser.add_argument('--print_every', type=int, default=100,
                    help='how many batches to wait before logging training status')
parser.add_argument('--save_results', action='store_true',
                    help='save output results')
parser.add_argument('--save_gt', action='store_true',
                    help='save low-resolution and high-resolution images together')

args = parser.parse_args()
template.set_template(args)

args.scale = list(map(lambda x: int(x), args.scale.split('+')))
args.data_train = args.data_train.split('+')
args.data_test = args.data_test.split('+')

if args.epochs == 0:
    args.epochs = 1e8

for arg in vars(args):
    if vars(args)[arg] == 'True':
        vars(args)[arg] = True
    elif vars(args)[arg] == 'False':
        vars(args)[arg] = False

# New options
parser.add_argument('--n_resgroups', type=int, default=10,
                    help='number of residual groups')
parser.add_argument('--reduction', type=int, default=16,
                    help='number of feature maps reduction')
parser.add_argument('--testpath', type=str, default='../test/DIV2K_val_LR_our',
                    help='dataset directory for testing')
parser.add_argument('--testset', type=str, default='Set5',
                    help='dataset name for testing')
parser.add_argument('--degradation', type=str, default='BI',
                    help='degradation model: BI, BD')

                    
args = parser.parse_args()
template.set_template(args)

args.scale = list(map(lambda x: int(x), args.scale.split('+')))

if args.epochs == 0:
    args.epochs = 1e8

for arg in vars(args):
    if vars(args)[arg] == 'True':
        vars(args)[arg] = True
    elif vars(args)[arg] == 'False':
        vars(args)[arg] = False

# Log specifications
parser.add_argument('--save', type=str, default='save_path',
                    help='file name to save')
parser.add_argument('--load', type=str, default='.',
                    help='file name to load')
parser.add_argument('--resume', action='store_true',
                    help='resume from the latest if true')
parser.add_argument('--print_every', type=int, default=100,
                    help='how many batches to wait before logging training status')
parser.add_argument('--save_images', default=True, action='store_false',
                    help='save images')

args = parser.parse_args()
template.set_template(args)

if args.epochs == 0:
    args.epochs = 1e8

for arg in vars(args):
    if vars(args)[arg] == 'True':
        vars(args)[arg] = True
    elif vars(args)[arg] == 'False':
        vars(args)[arg] = False