def METHOD_NAME():
    st_time = time.time()
    dialogs = request.json["dialogs"]
    selected_skill_names = []
    selected_responses = []
    selected_confidences = []
    selected_human_attributes = []
    selected_bot_attributes = []
    selected_attributes = []
    for i, dialog in enumerate(dialogs):
        hypotheses = [hyp for hyp in dialog["human_utterances"][-1]["hypotheses"]]
        if FILTER_TOXIC_OR_BADLISTED:
            hypotheses = filter_out_badlisted_or_toxic(hypotheses)
        hypotheses_texts = "\n".join([f'{h["skill_name"]} (conf={h["confidence"]}): {h["text"]}' for h in hypotheses])
        logger.info(f"Hypotheses: {hypotheses_texts}")
        dialog_context = [uttr["text"] for uttr in dialog["utterances"][-N_UTTERANCES_CONTEXT:]]
        selected_resp = select_response(
            dialog_context, hypotheses, dialog["human_utterances"][-1].get("attributes", {})
        )
        if selected_resp:
            best_id = find_most_similar_hypothesis(selected_resp, hypotheses)
            hypotheses[best_id].pop("text")
            selected_responses.append(selected_resp)
            selected_skill_names.append(hypotheses[best_id].pop("skill_name"))
            selected_confidences.append(hypotheses[best_id].pop("confidence"))
            selected_human_attributes.append(hypotheses[best_id].pop("human_attributes", {}))
            selected_bot_attributes.append(hypotheses[best_id].pop("bot_attributes", {}))
            hypotheses[best_id].pop("annotations", {})
            selected_attributes.append(hypotheses[best_id])
        else:
            logger.info("Select a response with the highest confidence.")
            selected_resp, best_id = select_response_by_scores(hypotheses, [hyp["confidence"] for hyp in hypotheses])
            selected_responses.append(hypotheses[best_id].pop("text"))
            selected_skill_names.append(hypotheses[best_id].pop("skill_name"))
            selected_confidences.append(hypotheses[best_id].pop("confidence"))
            selected_human_attributes.append(hypotheses[best_id].pop("human_attributes", {}))
            selected_bot_attributes.append(hypotheses[best_id].pop("bot_attributes", {}))
            hypotheses[best_id].pop("annotations", {})
            selected_attributes.append(hypotheses[best_id])
    total_time = time.time() - st_time
    logger.info(f"llm_based_response_selector exec time = {total_time:.3f}s")
    return jsonify(
        list(
            zip(
                selected_skill_names,
                selected_responses,
                selected_confidences,
                selected_human_attributes,
                selected_bot_attributes,
                selected_attributes,
            )
        )
    )