def METHOD_NAME(self, batch: Batch) -> Batch:
    """
    Return the image features as a Tensor of the correct type.
    Fill in missing feature vectors. Here, we require image features to be saved in
    `batch` as a Tensor for passing through the image encoder. This is required for
    data_parallel.
    """
    # Checks/formatting of batch.image
    bsz = self._get_batch_size(batch)
    if batch.image is None or len(batch.image) == 0:
        batch.image = [None] * bsz
    else:
        assert len(batch.image) == bsz
    # Process all image feature vectors, or add in zero vectors if missing
    processed_features_list = []
    processed_zero_features = self._process_image_features(
        torch.zeros((self.image_features_dim,))
    )
    for orig_features in batch.image:
        if isinstance(orig_features, torch.Tensor):
            processed_features_list.append(
                self._process_image_features(orig_features)
            )
        else:
            if orig_features is not None:
                warn_once(
                    'Unsupported image feature format. Image features will be ignored!'
                )
            processed_features_list.append(processed_zero_features)
    # Turn into batchsize x image_features_dim for DataParallel
    batch.image = torch.stack(processed_features_list)
    return batch