Instantiates an already-trained model, based on the experiment
        configuration and some optional overrides.

        Parameters
        ----------
        config: Params
            The configuration that was used to train the model. It should definitely
            have a `model` section, and should probably have a `trainer` section
            as well.
        serialization_dir: str = None
            The directory containing the serialized weights, parameters, and vocabulary
            of the model.
        weights_file: str = None
            By default we load the weights from `best.th` in the serialization
            directory, but you can override that value here.
        cuda_device: int = -1
            By default we load the model on the CPU, but if you want to load it
            for GPU usage you can specify the id of your GPU here


        Returns
        -------
        model: Model
            The model specified in the configuration, loaded with the serialized
            vocabulary and the trained weights.

def load(cls,
             config: Params,
             serialization_dir: str,
             weights_file: str = None,
             cuda_device: int = -1) -> 'Model':
        """
        Instantiates an already-trained model, based on the experiment
        configuration and some optional overrides.

        Parameters
        ----------
        config: Params
            The configuration that was used to train the model. It should definitely
            have a `model` section, and should probably have a `trainer` section
            as well.
        serialization_dir: str = None
            The directory containing the serialized weights, parameters, and vocabulary
            of the model.
        weights_file: str = None
            By default we load the weights from `best.th` in the serialization
            directory, but you can override that value here.
        cuda_device: int = -1
            By default we load the model on the CPU, but if you want to load it
            for GPU usage you can specify the id of your GPU here


        Returns
        -------
        model: Model
            The model specified in the configuration, loaded with the serialized
            vocabulary and the trained weights.
        """

        # Peak at the class of the model.
        model_type = config["model"]["type"]

        # Load using an overridable _load method.
        # This allows subclasses of Model to override _load.
        # pylint: disable=protected-access
        return cls.by_name(model_type)._load(config, serialization_dir, weights_file, cuda_device)