code: def resume_training(self, train_data, model_path, valid_data=None):
          
          restore_state = self.checkpointer.restore(model_path)
          loss_fn = self._get_loss_fn()
          self.train()
          self._train_model(
              train_data=train_data,
              loss_fn=loss_fn,
              valid_data=valid_data,
              restore_state=restore_state,
          )
docs: This model resume training of a classifier by reloading the appropriate state_dicts for each model
  
          Args:
             train_data: a tuple of Tensors (X,Y), a Dataset, or a DataLoader of
                  X (data) and Y (labels) for the train split
              model_path: the path to the saved checpoint for resuming training
              valid_data: a tuple of Tensors (X,Y), a Dataset, or a DataLoader of
                  X (data) and Y (labels) for the dev split