              mask = self._erode(mask)
          logger.trace(  # type: ignore
              "mask shape: %s, raw_mask shape: %s", mask.shape, raw_mask.shape)
          return mask, raw_mask
  
      def _get_mask(self,
                    detected_face: DetectedFace,
 
</s> remove                   detected_face: "DetectedFace",
 </s> add                   detected_face: DetectedFace, </s> remove         if not self.skip and self._do_erode:
 </s> add         if self._config.get("type") is not None and self._do_erode: </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         new_face = self._adjustments["box"].run(new_face)
 </s> add  </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]:
        if self._mask_type != "none":
            mask *= self._box

              mask = self._erode(mask)
          logger.trace(  # type: ignore
              "mask shape: %s, raw_mask shape: %s", mask.shape, raw_mask.shape)
        if self._mask_type != "none":
            mask *= self._box

          return mask, raw_mask
  
      def _get_mask(self,
                    detected_face: DetectedFace,
keep keep add keep keep keep keep
 convert: remove box blend plugin 
plugins/convert/mask/mask_blend.py