I want to run the demo,but i can;t find where to import data

parser = argparse.ArgumentParser(
description='Crystal Graph Convolutional Neural Networks')
parser.add_argument('data_options', metavar='OPTIONS', nargs='+',
help='dataset options, started with the path to root dir, '
'then other options')
parser.add_argument('--disable-cuda', action='store_true',
help='Disable CUDA')
parser.add_argument('-j', '--workers', default=1, type=int, metavar='N',
help='number of data loading workers (default: 0)')
parser.add_argument('--epochs', default=30, type=int, metavar='N',
help='number of total epochs to run (default: 30)')
parser.add_argument('--start-epoch', default=20, type=int, metavar='N',
help='manual epoch number (useful on restarts)')
parser.add_argument('-b', '--batch-size', default=10, type=int,
metavar='N', help='mini-batch size (default: 256)')
parser.add_argument('--lr', '--learning-rate', default=0.01, type=float,
metavar='LR', help='initial learning rate (default: '
'0.01)')
parser.add_argument('--lr-milestones', default=[100], nargs='+', type=int,
metavar='N', help='milestones for scheduler (default: '
'[100])')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
help='momentum')
parser.add_argument('--weight_decay', '--wd', default=5e-4, type=float,
metavar='W', help='weight decay (default: 0)')
parser.add_argument('--print-freq', '-p', default=10, type=int,
metavar='N', help='print frequency (default: 10)')
parser.add_argument('--resume', default='', type=str, metavar='PATH',
help='path to latest checkpoint (default: none)')
train_group = parser.add_mutually_exclusive_group()
train_group.add_argument('--train-ratio', default=0.8, type=float, metavar='N',
help='number of training data to be loaded (default none)')
train_group.add_argument('--train-size', default=None, type=int, metavar='N',
help='number of training data to be loaded (default none)')
valid_group = parser.add_mutually_exclusive_group()
valid_group.add_argument('--val-ratio', default=0.1, type=float, metavar='N',
help='percentage of validation data to be loaded (default '
'0.1)')
valid_group.add_argument('--val-size', default=None, type=int, metavar='N',
help='number of validation data to be loaded (default '
'1000)')
test_group = parser.add_mutually_exclusive_group()
test_group.add_argument('--test-ratio', default=0.1, type=float, metavar='N',
help='percentage of test data to be loaded (default 0.1)')
test_group.add_argument('--test-size', default=None, type=int, metavar='N',
help='number of test data to be loaded (default 1000)')

parser.add_argument('--optim', default='SGD', type=str, metavar='SGD',
help='choose an optimizer, SGD or Adam, (default: SGD)')
parser.add_argument('--atom-fea-len', default=64, type=int, metavar='N',
help='number of hidden atom features in conv layers')
parser.add_argument('--h-fea-len', default=128, type=int, metavar='N',
help='number of hidden features after pooling')
parser.add_argument('--disable-save-torch', action='store_true',
help='Do not save CIF PyTorch data as .pkl files')
parser.add_argument('--clean-torch', action='store_true',
help='Clean CIF PyTorch data .pkl files')

args = parser.parse_args(sys.argv[1:])

args.cuda = not args.disable_cuda and torch.cuda.is_available()

print(torch.cuda.is_available())

print('args.cuda', args.cuda)

best_mae_error = 1e10

def main():
global args, best_mae_error

# load data
dataset = CIFData(*args.data_options,
                  disable_save_torch=args.disable_save_torch)
collate_fn = collate_pool
train_loader, val_loader, test_loader = get_train_val_test_loader(
    dataset=dataset,
    collate_fn=collate_fn,
    batch_size=args.batch_size,
    train_ratio=args.train_ratio,
    num_workers=args.workers,
    val_ratio=args.val_ratio,
    test_ratio=args.test_ratio,
    pin_memory=args.cuda,
    train_size=args.train_size,
    val_size=args.val_size,
    test_size=args.test_size,
    return_test=True)

thanks