def METHOD_NAME(images: list[Image.Image]) -> np.ndarray:
    embedding_batches = []
    stub = get_triton_inference_stub()
    for image_batch in chunked(images, CLIP_MAX_BATCH_SIZE):
        request = generate_clip_embedding_request(image_batch)
        response = stub.ModelInfer(request)
        embedding_batch = np.frombuffer(
            response.raw_output_contents[0],
            dtype=np.float32,
        ).reshape((len(image_batch), -1))
        embedding_batches.append(embedding_batch)
    return np.concatenate(embedding_batches)