          logger.trace("returning: new_face shape %s", new_face.shape)
          return new_face
  
      def _get_image_mask(self, new_face, detected_face, predicted_mask, reference_face):
          """ Return any selected image mask and intersect with any box mask.
  
          Places the requested mask into the new face's Alpha channel, intersecting with any box
          mask that has already been applied.
  
          Parameters
 
</s> remove         Places the requested mask into the new face's Alpha channel, intersecting with any box
        mask that has already been applied.
 </s> add         Places the requested mask into the new face's Alpha channel. </s> remove     def process(self,  # type:ignore # pylint:disable=arguments-differ
                detected_face: "DetectedFace",
                sub_crop_offset: Optional[np.ndarray],
                centering: Literal["legacy", "face", "head"],
                predicted_mask: Optional[np.ndarray] = None) -> Tuple[np.ndarray, np.ndarray]:
 </s> add         Gradient is created from 1/16th distance from the edge of the face box and uses the
        parameters as provided for mask blend settings

        Parameters
        ----------
        output_size: int
            The size of the box that contains the swapped face

        Returns
        -------
        :class:`numpy.ndarray`
            The box mask
        """
        box = np.zeros((output_size, output_size, 1), dtype="float32")
        edge = output_size // 32
        box[edge:-edge, edge:-edge] = 1.0

        box = BlurMask(self._config["type"],
                       box, self._config["kernel_size"],
                       self._config["passes"]).blurred
        return box

    def run(self,
            detected_face: DetectedFace,
            sub_crop_offset: Optional[np.ndarray],
            centering: Literal["legacy", "face", "head"],
            predicted_mask: Optional[np.ndarray] = None) -> Tuple[np.ndarray, np.ndarray]: </s> remove                   detected_face: "DetectedFace",
 </s> add                   detected_face: DetectedFace, </s> remove             The mask sized to Faceswap model output with any requested blurring applied.
 </s> add             The requested mask. </s> remove         new_face = self._adjustments["box"].run(new_face)
 </s> add 
        """ Return any selected image mask
          logger.trace("returning: new_face shape %s", new_face.shape)
          return new_face
  
      def _get_image_mask(self, new_face, detected_face, predicted_mask, reference_face):
        """ Return any selected image mask
  
          Places the requested mask into the new face's Alpha channel, intersecting with any box
          mask that has already been applied.
  
          Parameters
keep keep keep keep replace keep keep keep keep keep
 convert: remove box blend plugin 
lib/convert.py