Ошибка: Неизвестная метрическая функция: 'function'. При попытке получить кэшированную загруженную модель (модель Keras Instance)

Ошибка: извлечение модели '' из кэша: Неизвестная метрическая функция: 'function'. Убедитесь, что вы используете keras.utils.custom_object_scope и что этот объект включен в область видимости. Подробности см. в https://www.tensorflow.org/guide/keras/save_and_serialize#registering_the_custom_object.

Получение строк множественной ML-модели и хранение загруженной модели в Redis

models = MLModel.objects.all() 
for model_entry in models:
        try:
            model_title = model_entry.title
            model_name = model_entry.name
            model_url = model_entry.file.url
            lock_name = f"lock:model:{model_title}"

            with redis_lock(lock_name) as acquired:
                if not acquired:
                    logger.info(f"Skipping model {model_name}, another worker is already loading it.")
                    continue

                logger.info(f"Loading model: {model_name} from URL: {model_url}")
                local_model_path = os.path.join(models_directory, f"{model_name}.h5")
                logger.info(f"Local file path: {local_model_path}")
            
                download_file(model_url, local_model_path)
                
                loss_type = model_entry.loss_type
                loss_code = model_entry.loss
                
                loss_function = load_loss(loss_type, loss_code)
                
                model = tf.keras.models.load_model(local_model_path, custom_objects={loss_type: loss_function})
                
                list_of_words = model_entry.list_of_words
                ml_utils_code = model_entry.ml_utils
            
                model_details = {
                    'name': model_name, 
                    'model': model,
                    'list_of_words': list_of_words, 
                    'ml_utils_code': ml_utils_code,
                    'loss_type': loss_type,
                    'loss_code': loss_code
                }

                cache_key = f"model:{model_title}"
                redis_client.set(cache_key, pickle.dumps(model_details))
                logger.info(f"Model {model_name} loaded and stored in cache with key: {model_title}.")
                        
        except Exception as e:
            logger.error(f"Error loading model {model_entry.name}: {e}")
    
    logger.info("All models loaded and stored in Redis cache.")

Теперь при получении конкретной загруженной модели из redis возникает ошибка

import logging

logger = logging.getLogger(__name__)
redis_client = redis.StrictRedis.from_url(settings.REDIS_URL)
 
def get_loaded_model(title):
    try:
        model_data = redis_client.get(f"model:{title}")
        if model_data:
            model_details = pickle.loads(model_data)
            
            ml_utils_code = model_details['ml_utils_code']
            ml_utils = load_ml_utils(ml_utils_code)

            loss_type = model_details['loss_type']

            logger.info(f"Custom Loss Type: {loss_type}")
            logger.info(f"Custom Loss Code: {loss_code}")

            loss_function = load_loss(loss_type, model_details['loss_code'])

            with custom_object_scope({loss_type: loss_function}):
                model = model_details['model']

            model_details['ml_utils'] = ml_utils
            return model_details, model
        else:
            logger.warning(f"Model with title '{title}' not found in cache.")
            return None
    except Exception as e:
        logger.error(f"Error retrieving model '{title}' from cache: {e}")
        return None

Traceback:

Error retrieving model '<title>' from cache: Unknown metric function: 'function'. Please ensure you are using a `keras.utils.custom_object_scope` and that this object is included in the scope. See https://www.tensorflow.org/guide/keras/save_and_serialize#registering_the_custom_object for details.
Unexpected error during model prediction setup for <title>: cannot unpack non-iterable NoneType object
Traceback (most recent call last):
  File "apps.py", line 70, in predict_with_model
    model_entry, model = get_loaded_model(title)
TypeError: cannot unpack non-iterable NoneType object

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