                                    self.dummy.shape[:2],
                                    interpolation=interp)[..., None]
          logger.trace(mask.shape)
          return mask
  
      def _crop_to_coverage(self, mask):
          """ Crop the mask to the correct dimensions based on coverage ratio.
 
</s> remove     def _generate_converter_arguments(arguments):
        """ Add the default converter arguments to the initial arguments.
 </s> add     def _generate_converter_arguments(arguments, available_masks):
        """ Add the default converter arguments to the initial arguments. Ensure the mask selection
        is available. </s> remove             mask = np.ones_like(self.dummy[:, :, 1], dtype="uint8") * 255
 </s> add             mask = np.ones_like(self.dummy[:, :, 1], dtype="float32")[..., None] </s> remove         return mask.astype("float32") / 255.0, raw_mask.astype("float32") / 255.0
 </s> add         return mask, raw_mask </s> remove         raw_mask = np.expand_dims(raw_mask, axis=-1) if raw_mask.ndim != 3 else raw_mask
        mask = np.expand_dims(mask, axis=-1) if mask.ndim != 3 else mask
 </s> add  </s> remove             mask = predicted_mask
 </s> add             mask = predicted_mask[..., None]
            mask = mask.astype("float32") / 255.0
                                    self.dummy.shape[:2],
                                    interpolation=interp)[..., None]
            mask = mask.astype("float32") / 255.0
          logger.trace(mask.shape)
          return mask
  
      def _crop_to_coverage(self, mask):
          """ Crop the mask to the correct dimensions based on coverage ratio.
keep add keep keep keep keep keep
 Convert - bugfixes
- Default mask to an available mask in Preview tool
- Correctly output predicted mask 
plugins/convert/mask/mask_blend.py