    def get_rank_classifications(
        self,
        batch_text: List[str],
        batch_images: List[List[Image.Image]],
        all_class_names: List[str],
        use_cache: bool,
        normalize_length: bool,
    ):
        """
        Returns a (B, |all_class_names|) tensor containing the logprobs for each class name.
        Args:
            batch_text: list of text strings, with the text "<image>" in place
                of any images to be included.
            batch_images: images to provide to model. Should be a list of lists,
                where each list contains the images for a single example.
            all_class_names: list of all class names.
            use_cache: whether to cache the context to speed up evaluations.
            normalize_length: whether to normalize logprobs by the length of the
                class name
        Returns:
            (B, |all_class_names|) tensor containing the logprobs for each class name.
        """
Returns a (B, |all_class_names|) tensor containing the logprobs for each class name.
Args:
batch_text: list of text strings, with the text "<image>" in place
of any images to be included.
batch_images: images to provide to model. Should be a list of lists,
where each list contains the images for a single example.
all_class_names: list of all class names.
use_cache: whether to cache the context to speed up evaluations.
normalize_length: whether to normalize logprobs by the length of the
class name
Returns:
(B, |all_class_names|) tensor containing the logprobs for each class name.
open_flamingo/eval/eval_model.py