  
      def save(self, is_exit: bool = False) -> None:
          """ Backup and save the model and state file.
  
          Notes
          -----
          The backup function actually backups the model from the previous save iteration rather than
          the current save iteration. This is not a bug, but protection against long save times, as
          models can get quite large, so renaming the current model file rather than copying it can
 
</s> remove     def save(self) -> None:
 </s> add     def save(self, is_exit: bool = False) -> None: </s> remove     def save(self) -> None:
 </s> add     def save(self, is_exit: bool = False) -> None: </s> remove     def __init__(self, plugin: "ModelBase", model_dir: str, is_predict: bool) -> None:
 </s> add     def __init__(self,
                 plugin: "ModelBase",
                 model_dir: str,
                 is_predict: bool,
                 save_optimizer: Literal["never", "always", "exit"]) -> None: </s> remove         self._io.save()  # pylint:disable=protected-access
 </s> add         self._io.save(is_exit=is_exit)
        Parameters
        ----------
        is_exit: bool, optional
            ``True`` if the save request has come from an exit process request otherwise ``False``
            Default: ``False``

  
      def save(self, is_exit: bool = False) -> None:
          """ Backup and save the model and state file.
  
        Parameters
        ----------
        is_exit: bool, optional
            ``True`` if the save request has come from an exit process request otherwise ``False``
            Default: ``False``

          Notes
          -----
          The backup function actually backups the model from the previous save iteration rather than
          the current save iteration. This is not a bug, but protection against long save times, as
          models can get quite large, so renaming the current model file rather than copying it can
keep keep keep add keep keep keep keep keep
 Training: Add setting option to save optimizer weights 
plugins/train/model/_base/io.py