def METHOD_NAME(args: Dict) -> Embeddings:
    """
    Deserializes the model from the model storage
    """
    target = args.pop("target", None)
    class_name = args.pop("class", LangchainEmbeddingHandler.DEFAULT_EMBEDDING_CLASS)
    if class_name in EMBEDDING_MODELS:
        LOG.info(
            f"Mapping the user friendly name {class_name} to the class name: {EMBEDDING_MODELS[class_name]}"
        )
        class_name = EMBEDDING_MODELS[class_name]
    MODEL_CLASS = get_langchain_class(class_name)
    serialized_dict = args
    model = MODEL_CLASS(**serialized_dict)
    if target is not None:
        args["target"] = target
    args["class"] = class_name
    return model