def _restore_checkpoint(self) -> int:
        """
        Restores the model and training state from the last saved checkpoint.
        This includes an epoch count and optimizer state, which is serialized separately
        from model parameters. This function should only be used to continue training -
        if you wish to load a model for inference/load parts of a model into a new
        computation graph, you should use the native Pytorch functions:
        `` model.load_state_dict(torch.load("/path/to/model/weights.th"))``

        If ``self._serialization_dir`` does not exist or does not contain any checkpointed weights,
        this function will do nothing and return 0.

        Returns
        -------
        epoch: int
            The epoch at which to resume training, which should be one after the epoch
            in the saved training state.
        """
        model_state, training_state = self._checkpointer.restore_checkpoint()

        if not training_state:
            # No checkpoint to restore, start at 0
            return 0

        self.model.load_state_dict(model_state)
        self.optimizer.load_state_dict(training_state["optimizer"])
        if self._learning_rate_scheduler is not None \
                and "learning_rate_scheduler" in training_state:
            self._learning_rate_scheduler.load_state_dict(training_state["learning_rate_scheduler"])
        if self._momentum_scheduler is not None and "momentum_scheduler" in training_state:
            self._momentum_scheduler.load_state_dict(training_state["momentum_scheduler"])
        training_util.move_optimizer_to_cuda(self.optimizer)

        # Currently the ``training_state`` contains a serialized ``MetricTracker``.
        if "metric_tracker" in training_state:
            self._metric_tracker.load_state_dict(training_state["metric_tracker"])
        # It used to be the case that we tracked ``val_metric_per_epoch``.
        elif "val_metric_per_epoch" in training_state:
            self._metric_tracker.clear()
            self._metric_tracker.add_metrics(training_state["val_metric_per_epoch"])
        # And before that we didn't track anything.
        else:
            self._metric_tracker.clear()

        if isinstance(training_state["epoch"], int):
            epoch_to_return = training_state["epoch"] + 1
        else:
            epoch_to_return = int(training_state["epoch"].split(".")[0]) + 1

        # For older checkpoints with batch_num_total missing, default to old behavior where
        # it is unchanged.
        batch_num_total = training_state.get("batch_num_total")
        if batch_num_total is not None:
            self._batch_num_total = batch_num_total

        return epoch_to_return

        Restores the model and training state from the last saved checkpoint.
        This includes an epoch count and optimizer state, which is serialized separately
        from model parameters. This function should only be used to continue training -
        if you wish to load a model for inference/load parts of a model into a new
        computation graph, you should use the native Pytorch functions:
        `` model.load_state_dict(torch.load("/path/to/model/weights.th"))``

        If ``self._serialization_dir`` does not exist or does not contain any checkpointed weights,
        this function will do nothing and return 0.

        Returns
        -------
        epoch: int
            The epoch at which to resume training, which should be one after the epoch
            in the saved training state.
        
gector/trainer.py