          self.landmarks = self.training_opts.get("landmarks", None)
          self._nearest_landmarks = None
          self.processing = ImageManipulation(model_input_size,
                                              model_output_size,
                                              training_opts.get("coverage_ratio", 0.625))
          logger.debug("Initialized %s", self.__class__.__name__)
  
      def set_mask_class(self):
          """ Set the mask function to use if using mask """
          mask_type = self.training_opts.get("mask_type", None)
 
</s> remove                      "batchers: '%s')", self.__class__.__name__, model, use_mask,
                     coverage_ratio, batchers)
 </s> add                      "preview_images: %s, batchers: '%s')", self.__class__.__name__, model,
                     use_mask, coverage_ratio, preview_images, batchers)
        self.preview_images = preview_images </s> remove         preview_images = self.config.get("preview_images", 14)
        preview_images = min(max(preview_images, 2), 16)
        self.training_opts["preview_images"] = preview_images
 </s> add  </s> remove         augment_color:      Perform random shifting of LAB colors
 </s> add         augment_color:      Perform random shifting of L*a*b* colors </s> remove         num_images = self.model.training_opts.get("preview_images", 14)
 </s> add         num_images = self.config.get("preview_images", 14)
                                            training_opts.get("coverage_ratio", 0.625),
                                            config)
          self.landmarks = self.training_opts.get("landmarks", None)
          self._nearest_landmarks = None
          self.processing = ImageManipulation(model_input_size,
                                              model_output_size,
                                            training_opts.get("coverage_ratio", 0.625),
                                            config)
          logger.debug("Initialized %s", self.__class__.__name__)
  
      def set_mask_class(self):
          """ Set the mask function to use if using mask """
          mask_type = self.training_opts.get("mask_type", None)
keep keep keep keep replace keep keep keep keep keep
 Expose Augmentation Options to config 
lib/training_data.py