              ``True`` if all actions have been completed on the output line otherwise ``False``
          """
          if self._command == "train" and self._capture_loss(output):
              return True
  
          if self._command == "effmpeg" and self._capture_ffmpeg(output):
              return True
  
 
</s> remove                 if self._command == "train" and output.startswith("Reading training images"):
                    print(output.strip(), file=sys.stdout)
 </s> add                 if self._process_training_determinate_function(output): </s> remove         if "[saved models]" not in output.strip().lower():
 </s> add         t_output = output.strip().lower()
        if "[saved model]" not in t_output or t_output.endswith("[saved model]"):
            # Not a saved model line or saving the model for a reason other than standard saving </s> remove             self._backup.backup_model(self._filename)
            # pylint:disable=protected-access
            self._backup.backup_model(self._plugin.state._filename)
 </s> add             self._backup.backup_model(self.filename)
            self._backup.backup_model(self._plugin.state.filename) </s> remove         return os.path.isfile(self._filename)
 </s> add         return os.path.isfile(self.filename) </s> remove     def save(self, is_exit: bool = False) -> None:
        """ Save the model to disk.

        Saves the serialized model, with weights, to the folder location specified when
        initializing the plugin. If loss has dropped on both sides of the model, then
        a backup is taken.

        Parameters
        ----------
        is_exit: bool, optional
            ``True`` if the save request has come from an exit process request otherwise ``False``
            Default: ``False``
        """
        self._io.save(is_exit=is_exit)

    def snapshot(self) -> None:
        """ Creates a snapshot of the model folder to the models parent folder, with the number
        of iterations completed appended to the end of the model name. """
        self._io.snapshot()

 </s> add 
        if self._process_training_determinate_function(output):
            return True

              ``True`` if all actions have been completed on the output line otherwise ``False``
          """
        if self._process_training_determinate_function(output):
            return True

          if self._command == "train" and self._capture_loss(output):
              return True
  
          if self._command == "effmpeg" and self._capture_ffmpeg(output):
              return True
  
keep add keep keep keep keep keep keep
 Learning Rate finder (#1341)

* Add LR Finder support structure

* Move trainer.Feeder to lib.training.generator

* Expose model.io

* Add lr_finder

* Update docs and locales

* Pre-PR fixups
  - Fix training graph not displaying
  - CI fixes
  - Switch lr finder progress to tqdm
  - Exit lr finder early on NaN
  - Display lr finder progress in GUI 
lib/gui/wrapper.py