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

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')

    for iteration in range(num_iterations):
        opt.zero_grad()
        meta_train_error = 0.0
        meta_train_accuracy = 0.0

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),
    ]
    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)

transforms.Normalize((0.1307,), (0.3081,)),
        lambda x: x.view(1, 28, 28),
    ])

    mnist_train = l2l.data.MetaDataset(MNIST(download_location,
                                             train=True,
                                             download=True,
                                             transform=transformations))

    train_tasks = l2l.data.TaskDataset(mnist_train,
                                       task_transforms=[
                                            l2l.data.transforms.NWays(mnist_train, ways),
                                            l2l.data.transforms.KShots(mnist_train, 2*shots),
                                            l2l.data.transforms.LoadData(mnist_train),
                                            l2l.data.transforms.RemapLabels(mnist_train),
                                            l2l.data.transforms.ConsecutiveLabels(mnist_train),
                                       ],
                                       num_tasks=1000)

    model = Net(ways)
    model.to(device)
    meta_model = l2l.algorithms.MAML(model, lr=maml_lr)
    opt = optim.Adam(meta_model.parameters(), lr=lr)
    loss_func = nn.NLLLoss(reduction='mean')

    for iteration in range(iterations):
        iteration_error = 0.0
        iteration_acc = 0.0
        for _ in range(tps):
            learner = meta_model.clone()
            train_task = train_tasks.sample()
            data, labels = train_task

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),
    ]
    valid_tasks = l2l.data.TaskDataset(valid_dataset,
                                       task_transforms=valid_transforms,
                                       num_tasks=600)

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),
    ]
    test_tasks = l2l.data.TaskDataset(test_dataset,
                                      task_transforms=test_transforms,
                                      num_tasks=600)

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])
    ]
    valid_tasks = l2l.data.TaskDataset(dataset,
                                       task_transforms=valid_transforms,
                                       num_tasks=1024)

    test_transforms = [
        l2l.data.transforms.FilterLabels(dataset, classes[1200:]),
        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])
    ]
    test_tasks = l2l.data.TaskDataset(dataset,
                                      task_transforms=test_transforms,
                                      num_tasks=1024)

    # Create model
    model = l2l.vision.models.OmniglotFC(28 ** 2, 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')

    for iteration in range(num_iterations):
        opt.zero_grad()
        meta_train_error = 0.0