How to use the torchfcn.models.VGG16 function in torchfcn

torchfcn.datasets.VOC2011ClassSeg(
            root, split='seg11valid', transform=True),
        batch_size=1, shuffle=False, **kwargs)

    # 2. model

    model = torchfcn.models.FCN8sAtOnce(n_class=21)
    start_epoch = 0
    start_iteration = 0
    if args.resume:
        checkpoint = torch.load(args.resume)
        model.load_state_dict(checkpoint['model_state_dict'])
        start_epoch = checkpoint['epoch']
        start_iteration = checkpoint['iteration']
    else:
        vgg16 = torchfcn.models.VGG16(pretrained=True)
        model.copy_params_from_vgg16(vgg16)
    if cuda:
        model = model.cuda()

    # 3. optimizer

    optim = torch.optim.SGD(
        [
            {'params': get_parameters(model, bias=False)},
            {'params': get_parameters(model, bias=True),
             'lr': args.lr * 2, 'weight_decay': 0},
        ],
        lr=args.lr,
        momentum=args.momentum,
        weight_decay=args.weight_decay)
    if args.resume:

torchfcn.datasets.VOC2011ClassSeg(
            root, split='seg11valid', transform=True),
        batch_size=1, shuffle=False, **kwargs)

    # 2. model

    model = torchfcn.models.FCN32s(n_class=21)
    start_epoch = 0
    start_iteration = 0
    if args.resume:
        checkpoint = torch.load(args.resume)
        model.load_state_dict(checkpoint['model_state_dict'])
        start_epoch = checkpoint['epoch']
        start_iteration = checkpoint['iteration']
    else:
        vgg16 = torchfcn.models.VGG16(pretrained=True)
        model.copy_params_from_vgg16(vgg16)
    if cuda:
        model = model.cuda()

    # 3. optimizer

    optim = torch.optim.SGD(
        [
            {'params': get_parameters(model, bias=False)},
            {'params': get_parameters(model, bias=True),
             'lr': args.lr * 2, 'weight_decay': 0},
        ],
        lr=args.lr,
        momentum=args.momentum,
        weight_decay=args.weight_decay)
    if args.resume:

batch_size=batch_size, shuffle=True, **kwargs)
    valid_loader = torch.utils.data.DataLoader(
        dataset_class(split='valid', transform=True),
        batch_size=batch_size, shuffle=False, **kwargs)

    # 2. model

    n_class = len(train_loader.dataset.class_names)
    model = torchfcn.models.FCN32s(n_class=n_class, nodeconv=cfg['nodeconv'])
    start_epoch = 0
    if resume:
        checkpoint = torch.load(resume)
        model.load_state_dict(checkpoint['model_state_dict'])
        start_epoch = checkpoint['epoch']
    else:
        vgg16 = torchfcn.models.VGG16(pretrained=True)
        model.copy_params_from_vgg16(vgg16, copy_fc8=False, init_upscore=False)
    if cuda:
        if torch.cuda.device_count() == 1:
            model = model.cuda()
        else:
            model = torch.nn.DataParallel(model).cuda()

    # 3. optimizer

    optim = torch.optim.Adam(model.parameters(), lr=cfg['lr'],
                             weight_decay=cfg['weight_decay'])
    if resume:
        optim.load_state_dict(checkpoint['optim_state_dict'])

    trainer = torchfcn.Trainer(
        cuda=cuda,