              return None
          logger.debug("Enabling TensorBoard Logging")
  
          logger.debug("Setting up TensorBoard Logging")
          log_dir = os.path.join(str(self._model.model_dir),
                                 f"{self._model.name}_logs",
                                 f"session_{self._model.state.session_id}")
          tensorboard = tf.keras.callbacks.TensorBoard(log_dir=log_dir,
                                                       histogram_freq=0,  # Must be 0 or hangs
                                                       write_graph=True,
 
</s> remove             output = get_folder(os.path.join(str(self._model.model_dir),
 </s> add             output = get_folder(os.path.join(str(self._model.io.model_dir), </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         filename = self._io._filename  # pylint:disable=protected-access
 </s> add         filename = self._io.filename </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 logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
 </s> add logger = logging.getLogger(__name__)
        log_dir = os.path.join(str(self._model.io.model_dir),
              return None
          logger.debug("Enabling TensorBoard Logging")
  
          logger.debug("Setting up TensorBoard Logging")
        log_dir = os.path.join(str(self._model.io.model_dir),
                                 f"{self._model.name}_logs",
                                 f"session_{self._model.state.session_id}")
          tensorboard = tf.keras.callbacks.TensorBoard(log_dir=log_dir,
                                                       histogram_freq=0,  # Must be 0 or hangs
                                                       write_graph=True,
keep keep keep keep replace 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 
plugins/train/trainer/_base.py