              logger.debug("Tracking variable modification: %s", self.name)
              var.trace("w",
                        lambda name, index, mode, cmd=self._command: self._modified_callback(cmd))
  
          if track_modified and self._command in ("train", "convert") and self.title == "Model Dir":
              var.trace("w", lambda name, index, mode, v=var: self._model_callback(v))
  
          return var
  
      @staticmethod
 
</s> remove             if item is None and key == "masks":
                logger.trace("Creating dummy masks. side: %s", side)
                masks = np.ones_like(batch[..., :1], dtype=batch.dtype)
                continue
 </s> add  </s> remove             logger.trace("Obtaining masks for batch. (key: %s side: %s)", key, side)
            masks = np.array([self._get_mask(item[side][filename], size)
                              for filename in filenames], dtype=batch.dtype)
            masks = self._resize_masks(size, masks)
 </s> add             if item is None and key == "masks":
                logger.trace("Creating dummy masks. side: %s", side)
                masks = np.ones_like(batch[..., :1], dtype=batch.dtype)
            else:
                logger.trace("Obtaining masks for batch. (key: %s side: %s)", key, side)
                masks = np.array([self._get_mask(item[side][filename], size)
                                  for filename in filenames], dtype=batch.dtype)
                masks = self._resize_masks(size, masks) </s> remove         if self._model.config["mouth_multiplier"] > 1:
 </s> add         if penalized_loss and self._model.config["mouth_multiplier"] > 1: </s> remove         if self._model.config["eye_multiplier"] > 1:
 </s> add         if penalized_loss and self._model.config["eye_multiplier"] > 1:
        if track_modified and self._command == "train" and self.title == "Model Dir":
              logger.debug("Tracking variable modification: %s", self.name)
              var.trace("w",
                        lambda name, index, mode, cmd=self._command: self._modified_callback(cmd))
  
        if track_modified and self._command == "train" and self.title == "Model Dir":
              var.trace("w", lambda name, index, mode, v=var: self._model_callback(v))
  
          return var
  
      @staticmethod
keep keep keep keep replace keep keep keep keep keep
 - Bugfix: Training - Disable loss multipliers if penalized loss not selected
- Training: Half mouth/eye multiplier defaults
- GUI: Remove analysis callback from convert tab 
lib/gui/control_helper.py