code: def load(cls,
               config: Params,
               serialization_dir: str,
               weights_file: str = None,
               cuda_device: int = -1) -> 'Model':
          
  
          # 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)
docs: 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.