def METHOD_NAME():
    st_time = time.time()
    bot_utterances = request.json["sentences"]
    human_utterances = request.json["last_human_utterances"]
    bot_utterances_sentences = [
        spacy_sent_tokenize(utterance) if isinstance(utterance, str) else utterance for utterance in bot_utterances
    ]
    dialog_ids = []
    inputs = []
    for i, bot_utterance_sents in enumerate(bot_utterances_sentences):
        if human_utterances[i]:
            if isinstance(human_utterances[i], str):
                context = spacy_sent_tokenize(human_utterances[i])[-1].lower()
            elif isinstance(human_utterances[i], list):
                context = human_utterances[i][-1].lower()
            else:
                context = ""
        else:
            context = ""
        for utterance in bot_utterance_sents:
            inputs += [f"{context} {EMPTY_SIGN} {utterance.lower()}"]
            dialog_ids += [i]
    responses = predict(inputs)  # list of dicts, dict keys - classes, dict values - probas
    final_responses = recombine_responses(responses, dialog_ids, len(bot_utterances))
    logging.info(f"midas_classification exec time {time.time() - st_time}")
    return jsonify([{"batch": final_responses}])