def METHOD_NAME(self, model):
    state_dict = torch.load(model, map_location=lambda storage, loc: storage)
    state_dict = {k: v for k, v in state_dict.items() if not (k.startswith("classification_headers") or k.startswith("regression_headers"))}
    model_dict = self.state_dict()
    model_dict.update(state_dict)
    self.load_state_dict(model_dict)
    self.classification_headers.apply(_xavier_init_)
    self.regression_headers.apply(_xavier_init_)