  
      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
          ----------
          new_face: :class:`numpy.ndarray`
              The swapped face received from the faceswap model, with any box mask applied
 
</s> remove         """ Return any selected image mask and intersect with any box mask.
 </s> add         """ Return any selected image mask </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             The mask sized to Faceswap model output with any requested blurring applied.
 </s> add             The requested mask. </s> remove         logger.trace(  # type: ignore
            "detected_face: %s, sub_crop_offset: %s, centering: '%s', predicted_mask: %s",
            detected_face, sub_crop_offset, centering, predicted_mask is not None)
 </s> add         logger.trace("Performing mask adjustment: (detected_face: %s, "  # type: ignore
                     "sub_crop_offset: %s, centering: '%s', predicted_mask: %s",
                     detected_face, sub_crop_offset, centering, predicted_mask is not None) </s> remove                   detected_face: "DetectedFace",
 </s> add                   detected_face: DetectedFace,
        Places the requested mask into the new face's Alpha channel.
  
      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.
        Places the requested mask into the new face's Alpha channel.
  
          Parameters
          ----------
          new_face: :class:`numpy.ndarray`
              The swapped face received from the faceswap model, with any box mask applied
keep keep keep keep replace replace keep keep keep keep keep
 convert: remove box blend plugin 
lib/convert.py