          """ Toggle the mask overlay on or off depending on user input. """
          if not (self._model.config["learn_mask"] or self._model.config["penalized_mask_loss"]):
              return
          display_mask = not self._display_mask
          print("\n")  # Break to not garble loss output
          logger.info("Toggling mask display %s...", "on" if display_mask else "off")
          self._display_mask = display_mask
  
      def show_sample(self):
          """ Compile a preview image.
 
</s> remove     def show_sample(self):
 </s> add     def show_sample(self) -> np.ndarray: </s> remove     def toggle_mask_display(self):
 </s> add     def toggle_mask_display(self) -> None: </s> remove         self.images = {}
 </s> add         self.images: Dict[Literal["a", "b"], List[np.ndarray]] = {} </s> remove         logger.verbose("Enabled TensorBoard Logging")
 </s> add         logger.verbose("Enabled TensorBoard Logging")  # type: ignore </s> remove     def toggle_mask(self):
 </s> add     def toggle_mask(self) -> None:
        print("")  # Break to not garble loss output
          """ Toggle the mask overlay on or off depending on user input. """
          if not (self._model.config["learn_mask"] or self._model.config["penalized_mask_loss"]):
              return
          display_mask = not self._display_mask
        print("")  # Break to not garble loss output
          logger.info("Toggling mask display %s...", "on" if display_mask else "off")
          self._display_mask = display_mask
  
      def show_sample(self):
          """ Compile a preview image.
keep keep keep keep replace keep keep keep keep keep
 Data Augmentation update (#1263)

- lib.detected_face
    - Subclass Masks for Landmark based masks
    - Add training mask propery + methods to DetectedFace
  - lib.training_training
    - subclass TrainingDataGenerator for training and preview data
    - Split cache into own module
    - Reduce thread count to 1 to prevent image corruption + data re-use
    - Process on largest model input/output size rather than stored image size
    - Size and crop masks during caching stage
    - Implement ring buffer for data flow
    - Fix preview reload bug
  - augmentation
    - typing
    - switch color aug order
    - better initialization
    - Fix warp + landmark warp to correctly apply at different image scales
    - Slightly improved warp caching
    - Don't store whether image is_preview. Handle all data as training images implicitly
  - plugins.trainer: Typing and fixes to work with trainingdata refactor 
plugins/train/trainer/_base.py