     
          # save the final model
          model_param_dict = {'step':conf.dl_steps,'model_state_params':self.model.state_dict(),...}
          torch.save(model_param_dict,output_model_file_name.format(conf.dl_steps))
                      
          
      <b>def get_pred_fn(self,model_file=None)</b>:
          # This is the function that is called to setup the model to start predicting. If model_file is not None, then the weights should be loaded from the model_file. If model_file is None, then load