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

def resume_training(self, train_data, model_path, valid_data=None):
        """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
        """
        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,
        )