      if avg_scale >= 1.:
          interpolators = cv2.INTER_CUBIC, cv2.INTER_AREA
      else:
          interpolators = cv2.INTER_AREA, cv2.INTER_CUBIC
      logger.trace("interpolator: %s, inverse interpolator: %s", interpolators[0], interpolators[1])
      return interpolators
  
  
  def transform_image(image, matrix, size, padding=0):
      """ Perform transformation on an image, applying the given size and padding to the matrix.
 
</s> remove def transform_image(image, matrix, size, padding=0):
 </s> add def transform_image(image: np.ndarray,
                    matrix: np.ndarray,
                    size: int,
                    padding: int = 0) -> np.ndarray: </s> remove def get_matrix_scaling(matrix):
 </s> add def get_matrix_scaling(matrix: np.ndarray) -> Tuple[int, int]: </s> remove                 logger.trace("interpolators: %s", interpolators)
                self._cache["interpolators"][0] = interpolators
        return self._cache["interpolators"][0]
 </s> add                 logger.trace("interpolators: %s", interpolators)  # type: ignore
                self._cache.interpolators = interpolators
        return self._cache.interpolators </s> remove         with self._cache["interpolators"][1]:
            if self._cache["interpolators"][0] is None:
 </s> add         with self._cache.lock("interpolators"):
            if not any(self._cache.interpolators): </s> remove     def interpolators(self):
 </s> add     def interpolators(self) -> Tuple[int, int]:
    logger.trace("interpolator: %s, inverse interpolator: %s",  # type: ignore
                 interpolators[0], interpolators[1])
      if avg_scale >= 1.:
          interpolators = cv2.INTER_CUBIC, cv2.INTER_AREA
      else:
          interpolators = cv2.INTER_AREA, cv2.INTER_CUBIC
    logger.trace("interpolator: %s, inverse interpolator: %s",  # type: ignore
                 interpolators[0], interpolators[1])
      return interpolators
  
  
  def transform_image(image, matrix, size, padding=0):
      """ Perform transformation on an image, applying the given size and padding to the matrix.
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