def get_image_embedding_batch(
        self, img_file_paths: List[ImageType], show_progress: bool = False
    ) -> List[Embedding]:
        """Get a list of image embeddings, with batching."""
        cur_batch: List[ImageType] = []
        result_embeddings: List[Embedding] = []

        queue_with_progress = enumerate(
            get_tqdm_iterable(
                img_file_paths, show_progress, "Generating image embeddings"
            )
        )

        for idx, img_file_path in queue_with_progress:
            cur_batch.append(img_file_path)
            if (
                idx == len(img_file_paths) - 1
                or len(cur_batch) == self.embed_batch_size
            ):
                # flush
                with self.callback_manager.event(
                    CBEventType.EMBEDDING,
                    payload={EventPayload.SERIALIZED: self.to_dict()},
                ) as event:
                    embeddings = self._get_image_embeddings(cur_batch)
                    result_embeddings.extend(embeddings)
                    event.on_end(
                        payload={
                            EventPayload.CHUNKS: cur_batch,
                            EventPayload.EMBEDDINGS: embeddings,
                        },
                    )
                cur_batch = []

        return result_embeddings
Get a list of image embeddings, with batching.
llama-index-core/llama_index/core/embeddings/multi_modal_base.py