How to use the baal.active.ActiveLearningDataset function in baal

transforms.ToTensor(),
         transforms.Normalize(3 * [0.5], 3 * [0.5]), ])
    test_transform = transforms.Compose(
        [
            transforms.Resize((224, 224)),
            transforms.ToTensor(),
            transforms.Normalize(3 * [0.5], 3 * [0.5]),
        ]
    )
    # Note: We use the test set here as an example. You should make your own validation set.
    train_ds = datasets.CIFAR10('.', train=True,
                                transform=transform, target_transform=None, download=True)
    test_set = datasets.CIFAR10('.', train=False,
                                transform=test_transform, target_transform=None, download=True)

    active_set = ActiveLearningDataset(train_ds, pool_specifics={'transform': test_transform})

    # We start labeling randomly.
    active_set.label_randomly(initial_pool)
    return active_set, test_set