          """
          retval = np.expand_dims(points, axis=1)
          mat = cv2.invertAffineTransform(self.adjusted_matrix) if invert else self.adjusted_matrix
          retval = cv2.transform(retval, mat, retval.shape).squeeze()
          logger.trace("invert: %s, Original points: %s, transformed points: %s",
                       invert, points, retval)
          return retval
  
      def extract_face(self, image):
          """ Extract the face from a source image and populate :attr:`face`. If an image is not
 
</s> remove     def extract_face(self, image):
 </s> add     def extract_face(self, image: Optional[np.ndarray]) -> Optional[np.ndarray]: </s> remove     logger.trace("transformed matrix: %s, final image shape: %s", mat, image.shape)
 </s> add     logger.trace("transformed matrix: %s, final image shape: %s",  # type: ignore
                 mat, image.shape) </s> remove     def _convert_centering(self, image):
 </s> add     def _convert_centering(self, image: np.ndarray) -> np.ndarray: </s> remove         return dict(pose=[None, Lock()],
                    original_roi=[None, Lock()],
                    landmarks=[None, Lock()],
                    landmarks_normalized=[None, Lock()],
                    average_distance=[None, Lock()],
                    adjusted_matrix=[None, Lock()],
                    interpolators=[None, Lock()],
                    head_size=[dict(), Lock()],
                    cropped_roi=[dict(), Lock()],
                    cropped_size=[dict(), Lock()],
                    cropped_slices=[dict(), Lock()])

    def transform_points(self, points, invert=False):
 </s> add         retval = {_type: round((size * (coverage_ratio - (1 - _EXTRACT_RATIOS[_type]))) / 2)
                  for _type in get_args(Literal["legacy", "face", "head"])}
        logger.trace(retval)  # type: ignore
        return retval

    def transform_points(self, points: np.ndarray, invert: bool = False) -> np.ndarray: </s> remove         if self._is_aligned and self._centering != "head":  # Crop out the sub face from full head
 </s> add         if self._is_aligned and (self._centering != self._source_centering or
                                 self._coverage_ratio != 1.0):
            # Crop out the sub face from full head
        logger.trace("invert: %s, Original points: %s, transformed points: %s",  # type: ignore
          """
          retval = np.expand_dims(points, axis=1)
          mat = cv2.invertAffineTransform(self.adjusted_matrix) if invert else self.adjusted_matrix
          retval = cv2.transform(retval, mat, retval.shape).squeeze()
        logger.trace("invert: %s, Original points: %s, transformed points: %s",  # type: ignore
                       invert, points, retval)
          return retval
  
      def extract_face(self, image):
          """ Extract the face from a source image and populate :attr:`face`. If an image is not
keep keep keep keep replace keep keep keep keep keep
 lib.align.aligned_face updates
  - Typing
  - Legacy support for pre-aligned faces
  - Coverage support for pre-aligned faces
  - Standardized retrieval of sub-crops 
lib/align/aligned_face.py