How to use the learn2learn.data function in learn2learn

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

    test_transforms = [
        l2l.data.transforms.NWays(test_dataset, ways),

param_str = f'omniglot_nt={args.n_train}_kt={args.k_train}_qt={args.q_train}_' \
        f'nv={args.n_test}_kv={args.k_test}_qv={args.q_test}'

    filepath = f'./data/{param_str}.pth'

    omniglot = FullOmniglot(root='./data',
                            transform=transforms.Compose([
                                l2l.vision.transforms.RandomDiscreteRotation(
                                    [0.0, 90.0, 180.0, 270.0]),
                                transforms.Resize(28, interpolation=LANCZOS),
                                transforms.ToTensor(),
                                lambda x: 1.0 - x,
                            ]),
                            download=True)
    omniglot = l2l.data.MetaDataset(omniglot)
    classes = list(range(1623))
    random.shuffle(classes)
    train_generator = l2l.data.TaskGenerator(dataset=omniglot,
                                             ways=args.k_train,
                                             classes=classes[:1100],
                                             tasks=20000)
    valid_generator = l2l.data.TaskGenerator(dataset=omniglot,
                                             ways=args.k_test,
                                             classes=classes[1100:1200],
                                             tasks=1024)
    test_generator = l2l.data.TaskGenerator(dataset=omniglot,
                                            ways=args.k_test,
                                            classes=classes[1200:],
                                            tasks=1024)

    model = OmniglotCNN()

random.seed(seed)
    np.random.seed(seed)
    th.manual_seed(seed)
    device = th.device('cpu')
    if cuda:
        th.cuda.manual_seed(seed)
        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)

# 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)

    test_transforms = [
        NWays(test_dataset, ways),

cuda=True,
        seed=42,
):
    random.seed(seed)
    np.random.seed(seed)
    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),