          """
          logger.debug("Initializing constants.")
  
          # Transform
          tform_shift = (int(self._config.get("shift_range", 5)) / 100) * self._processing_size
  
          # Color Aug
          amount_l = int(self._config.get("color_lightness", 30)) / 100
          amount_ab = int(self._config.get("color_ab", 8)) / 100
          lab_adjust = np.array([amount_l, amount_ab, amount_ab], dtype="float32")
 
</s> remove         amount_l = int(self._config.get("color_lightness", 30)) / 100
        amount_ab = int(self._config.get("color_ab", 8)) / 100
 </s> add         amount_l = int(color_lightness) / 100
        amount_ab = int(color_ab) / 100 </s> remove                               clahe_chance=int(self._config.get("color_clahe_chance", 50)) / 100,
                              clahe_max_size=int(self._config.get("color_clahe_max_size", 4)),
 </s> add                               clahe_chance=color_clahe_chance / 100,
                              clahe_max_size=color_clahe_max_size, </s> remove                               transform_rotation=int(self._config.get("rotation_range", 10)),
                              transform_zoom=int(self._config.get("zoom_amount", 5)) / 100,
 </s> add                               transform_rotation=rotation_range,
                              transform_zoom=zoom_amount / 100, </s> remove         indices = np.where(randoms > int(self._config.get("random_flip", 50)) / 100)[0]
 </s> add         flip_chance = self._config.get("random_flip", 50)
        assert isinstance(flip_chance, int)
        indices = np.where(randoms > flip_chance / 100)[0]
        tform_shift = (shift_range / 100) * self._processing_size
          """
          logger.debug("Initializing constants.")
  
          # Transform
        tform_shift = (shift_range / 100) * self._processing_size
  
          # Color Aug
          amount_l = int(self._config.get("color_lightness", 30)) / 100
          amount_ab = int(self._config.get("color_ab", 8)) / 100
          lab_adjust = np.array([amount_l, amount_ab, amount_ab], dtype="float32")
keep keep keep keep replace keep keep keep keep keep
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lib/training/augmentation.py