How to use the learn2learn.data.transforms.NWays function in learn2learn

th.manual_seed(seed)
    device = th.device('cpu')
    if cuda and th.cuda.device_count():
        th.cuda.manual_seed(seed)
        device = th.device('cuda')

    # Create Datasets
    train_dataset = l2l.vision.datasets.MiniImagenet(root='./data', mode='train')
    valid_dataset = l2l.vision.datasets.MiniImagenet(root='./data', mode='validation')
    test_dataset = l2l.vision.datasets.MiniImagenet(root='./data', mode='test')
    train_dataset = l2l.data.MetaDataset(train_dataset)
    valid_dataset = l2l.data.MetaDataset(valid_dataset)
    test_dataset = l2l.data.MetaDataset(test_dataset)

    train_transforms = [
        l2l.data.transforms.NWays(train_dataset, ways),
        l2l.data.transforms.KShots(train_dataset, 2*shots),
        l2l.data.transforms.LoadData(train_dataset),
        l2l.data.transforms.RemapLabels(train_dataset),
        l2l.data.transforms.ConsecutiveLabels(train_dataset),
    ]
    train_tasks = l2l.data.TaskDataset(train_dataset,
                                       task_transforms=train_transforms,
                                       num_tasks=20000)

    valid_transforms = [
        l2l.data.transforms.NWays(valid_dataset, ways),
        l2l.data.transforms.KShots(valid_dataset, 2*shots),
        l2l.data.transforms.LoadData(valid_dataset),
        l2l.data.transforms.ConsecutiveLabels(train_dataset),
        l2l.data.transforms.RemapLabels(valid_dataset),
    ]

task_transforms=train_transforms,
                                       num_tasks=20000)

    valid_transforms = [
        l2l.data.transforms.NWays(valid_dataset, ways),
        l2l.data.transforms.KShots(valid_dataset, 2*shots),
        l2l.data.transforms.LoadData(valid_dataset),
        l2l.data.transforms.ConsecutiveLabels(train_dataset),
        l2l.data.transforms.RemapLabels(valid_dataset),
    ]
    valid_tasks = l2l.data.TaskDataset(valid_dataset,
                                       task_transforms=valid_transforms,
                                       num_tasks=600)

    test_transforms = [
        l2l.data.transforms.NWays(test_dataset, ways),
        l2l.data.transforms.KShots(test_dataset, 2*shots),
        l2l.data.transforms.LoadData(test_dataset),
        l2l.data.transforms.RemapLabels(test_dataset),
        l2l.data.transforms.ConsecutiveLabels(train_dataset),
    ]
    test_tasks = l2l.data.TaskDataset(test_dataset,
                                      task_transforms=test_transforms,
                                      num_tasks=600)

    # Create model
    model = l2l.vision.models.MiniImagenetCNN(ways)
    model.to(device)
    maml = l2l.algorithms.MAML(model, lr=fast_lr, first_order=False)
    opt = optim.Adam(maml.parameters(), meta_lr)
    loss = nn.CrossEntropyLoss(size_average=True, reduction='mean')

task_transforms=train_transforms,
                                       num_tasks=20000)

    valid_transforms = [
        NWays(valid_dataset, ways),
        KShots(valid_dataset, 2*shots),
        LoadData(valid_dataset),
        ConsecutiveLabels(train_dataset),
        RemapLabels(valid_dataset),
    ]
    valid_tasks = l2l.data.TaskDataset(valid_dataset,
                                       task_transforms=valid_transforms,
                                       num_tasks=600)

    test_transforms = [
        NWays(test_dataset, ways),
        KShots(test_dataset, 2*shots),
        LoadData(test_dataset),
        RemapLabels(test_dataset),
        ConsecutiveLabels(train_dataset),
    ]
    test_tasks = l2l.data.TaskDataset(test_dataset,
                                      task_transforms=test_transforms,
                                      num_tasks=600)

    # Create model
    model = l2l.vision.models.MiniImagenetCNN(ways)
    model.to(device)
    maml = l2l.algorithms.MAML(model, lr=fast_lr, first_order=False)
    opt = optim.Adam(maml.parameters(), meta_lr)
    loss = nn.CrossEntropyLoss(reduction='mean')

valid_dataset = l2l.data.MetaDataset(valid_dataset)
    test_dataset = l2l.data.MetaDataset(test_dataset)

    train_transforms = [
        NWays(train_dataset, ways),
        KShots(train_dataset, 2*shots),
        LoadData(train_dataset),
        RemapLabels(train_dataset),
        ConsecutiveLabels(train_dataset),
    ]
    train_tasks = l2l.data.TaskDataset(train_dataset,
                                       task_transforms=train_transforms,
                                       num_tasks=20000)

    valid_transforms = [
        NWays(valid_dataset, ways),
        KShots(valid_dataset, 2*shots),
        LoadData(valid_dataset),
        ConsecutiveLabels(train_dataset),
        RemapLabels(valid_dataset),
    ]
    valid_tasks = l2l.data.TaskDataset(valid_dataset,
                                       task_transforms=valid_transforms,
                                       num_tasks=600)

    test_transforms = [
        NWays(test_dataset, ways),
        KShots(test_dataset, 2*shots),
        LoadData(test_dataset),
        RemapLabels(test_dataset),
        ConsecutiveLabels(train_dataset),
    ]

th.manual_seed(seed)
    device = th.device('cpu')
    if cuda and th.cuda.device_count():
        th.cuda.manual_seed(seed)
        device = th.device('cuda')

    # Create Datasets
    train_dataset = l2l.vision.datasets.MiniImagenet(root='./data', mode='train')
    valid_dataset = l2l.vision.datasets.MiniImagenet(root='./data', mode='validation')
    test_dataset = l2l.vision.datasets.MiniImagenet(root='./data', mode='test')
    train_dataset = l2l.data.MetaDataset(train_dataset)
    valid_dataset = l2l.data.MetaDataset(valid_dataset)
    test_dataset = l2l.data.MetaDataset(test_dataset)

    train_transforms = [
        NWays(train_dataset, ways),
        KShots(train_dataset, 2*shots),
        LoadData(train_dataset),
        RemapLabels(train_dataset),
        ConsecutiveLabels(train_dataset),
    ]
    train_tasks = l2l.data.TaskDataset(train_dataset,
                                       task_transforms=train_transforms,
                                       num_tasks=20000)

    valid_transforms = [
        NWays(valid_dataset, ways),
        KShots(valid_dataset, 2*shots),
        LoadData(valid_dataset),
        ConsecutiveLabels(train_dataset),
        RemapLabels(valid_dataset),
    ]

device = th.device('cuda')

    omniglot = l2l.vision.datasets.FullOmniglot(root='./data',
                                                transform=transforms.Compose([
                                                    transforms.Resize(28, interpolation=LANCZOS),
                                                    transforms.ToTensor(),
                                                    lambda x: 1.0 - x,
                                                ]),
                                                download=True)
    dataset = l2l.data.MetaDataset(omniglot)
    classes = list(range(1623))
    random.shuffle(classes)

    train_transforms = [
        l2l.data.transforms.FilterLabels(dataset, classes[:1100]),
        l2l.data.transforms.NWays(dataset, ways),
        l2l.data.transforms.KShots(dataset, 2*shots),
        l2l.data.transforms.LoadData(dataset),
        l2l.data.transforms.RemapLabels(dataset),
        l2l.data.transforms.ConsecutiveLabels(dataset),
        l2l.vision.transforms.RandomClassRotation(dataset, [0.0, 90.0, 180.0, 270.0])
    ]
    train_tasks = l2l.data.TaskDataset(dataset,
                                       task_transforms=train_transforms,
                                       num_tasks=20000)

    valid_transforms = [
        l2l.data.transforms.FilterLabels(dataset, classes[1100:1200]),
        l2l.data.transforms.NWays(dataset, ways),
        l2l.data.transforms.KShots(dataset, 2*shots),
        l2l.data.transforms.LoadData(dataset),
        l2l.data.transforms.RemapLabels(dataset),