def METHOD_NAME(
    name,
    image_transforms=[],
    image_column="img",
    label_column="label",
    text_column="txt",
    split='train',
    image_key='image',
    caption_key='txt',
    ):
    """Make huggingface dataset with appropriate list of transforms applied
    """
    ds = load_dataset(name, split=split)
    image_transforms = [instantiate_from_config(tt) for tt in image_transforms]
    image_transforms.extend([transforms.ToTensor(),
                                transforms.Lambda(lambda x: rearrange(x * 2. - 1., 'c h w -> h w c'))])
    tform = transforms.Compose(image_transforms)
    assert image_column in ds.column_names, f"Didn't find column {image_column} in {ds.column_names}"
    assert label_column in ds.column_names, f"Didn't find column {label_column} in {ds.column_names}"
    def pre_process(examples):
        processed = {}
        processed[image_key] = [tform(im) for im in examples[image_column]]
        label_to_text_dict = {0: "airplane", 1: "automobile", 2: "bird", 3: "cat", 4: "deer", 5: "dog", 6: "frog", 7: "horse", 8: "ship", 9: "truck"}
        processed[caption_key] = [label_to_text_dict[label] for label in examples[label_column]]
        return processed
    ds.set_transform(pre_process)
    return ds