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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _load(cls, config: Params, serialization_dir: str, weights_file: str = None, cuda_device: int = -1) -> 'Model': """ Ensembles don't have vocabularies or weights of their own, so they override _load. """

        model_params = config.get('model')

        # The experiment config tells us how to _train_ a model, including where to get pre-trained
        # embeddings from.  We're now _loading_ the model, so those embeddings will already be
        # stored in our weights.  We don't need any pretrained weight file anymore, and we don't
        # want the code to look for it, so we remove it from the parameters here.
        remove_pretrained_embedding_params(model_params)
        model = Model.from_params(vocab=None, params=model_params)

        # Force model to cpu or gpu, as appropriate, to make sure that the embeddings are
        # in sync with the weights
        if cuda_device >= 0:
            model.cuda(cuda_device)
        else:
            model.cpu()

        return model