How to use the learn2learn.algorithms.MAML function in learn2learn

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
        meta_valid_error = 0.0
        meta_valid_accuracy = 0.0
        meta_test_error = 0.0
        meta_test_accuracy = 0.0
        for task in range(meta_batch_size):
            # Compute meta-training loss
            learner = maml.clone()
            batch = train_tasks.sample()
            evaluation_error, evaluation_accuracy = fast_adapt(batch,

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
        meta_train_accuracy = 0.0
        meta_valid_error = 0.0
        meta_valid_accuracy = 0.0
        meta_test_error = 0.0
        meta_test_accuracy = 0.0
        for task in range(meta_batch_size):
            # Compute meta-training loss
            learner = maml.clone()
            batch = train_tasks.sample()
            evaluation_error, evaluation_accuracy = fast_adapt(batch,