def handle(self, source, copy_to_local=False, normalize=True,
               split=None, classification_mode=False, **transform_args):
        """

        Args:
            source:
            copy_to_local:
            normalize:
            **transform_args:

        Returns:

        """
        Dataset = self.make_indexing(CelebA)
        data_path = self.get_path(source)

        if copy_to_local:
            data_path = self.copy_to_local_path(data_path)

        if normalize and isinstance(normalize, bool):
            normalize = [(0.5, 0.5, 0.5), (0.5, 0.5, 0.5)]

        if classification_mode:
            train_transform = transforms.Compose([
                transforms.RandomResizedCrop(64),
                transforms.RandomHorizontalFlip(),
                transforms.ToTensor(),
                transforms.Normalize(*normalize),
            ])
            test_transform = transforms.Compose([
                transforms.Resize(64),
                transforms.CenterCrop(64),
                transforms.ToTensor(),
                transforms.Normalize(*normalize),
            ])
        else:
            train_transform = build_transforms(normalize=normalize,
                                               **transform_args)
            test_transform = train_transform

        if split is None:
            train_set = Dataset(root=data_path, transform=train_transform,
                                download=True)
            test_set = Dataset(root=data_path, transform=test_transform)
        else:
            train_set, test_set = self.make_split(
                data_path, split, Dataset, train_transform, test_transform)
        input_names = ['images', 'labels', 'attributes']

        dim_c, dim_x, dim_y = train_set[0][0].size()
        dim_l = len(train_set.classes)
        dim_a = train_set.attributes[0].shape[0]

        dims = dict(x=dim_x, y=dim_y, c=dim_c, labels=dim_l, attributes=dim_a)
        self.add_dataset('train', train_set)
        self.add_dataset('test', test_set)
        self.set_input_names(input_names)
        self.set_dims(**dims)

        self.set_scale((-1, 1)) 

def _handle_STL(self, Dataset, data_path, transform=None,
                    labeled_only=False, stl_center_crop=False,
                    stl_resize_only=False, stl_no_resize=False):
        normalize = transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))

        if stl_no_resize:
            train_transform = transforms.Compose([
                transforms.RandomHorizontalFlip(),
                transforms.ToTensor(),
                normalize,
            ])
            test_transform = transforms.Compose([
                transforms.ToTensor(),
                normalize,
            ])
        else:
            if stl_center_crop:
                tr_trans = transforms.CenterCrop(64)
                te_trans = transforms.CenterCrop(64)
            elif stl_resize_only:
                tr_trans = transforms.Resize(64)
                te_trans = transforms.Resize(64)
            elif stl_no_resize:
                pass
            else:
                tr_trans = transforms.RandomResizedCrop(64)
                te_trans = transforms.Resize(64)

            train_transform = transforms.Compose([
                tr_trans,
                transforms.RandomHorizontalFlip(),
                transforms.ToTensor(),
                normalize,
            ])
            test_transform = transforms.Compose([
                te_trans,
                transforms.ToTensor(),
                normalize,
            ])
        if labeled_only:
            split = 'train'
        else:
            split = 'train+unlabeled'
        train_set = Dataset(
            data_path, split=split, transform=train_transform, download=True)
        test_set = Dataset(
            data_path, split='test', transform=test_transform, download=True)
        return train_set, test_set