          logger.trace("offset: %s", offset)
          return offset
  
  
  def _umeyama(source, destination, estimate_scale):
      """Estimate N-D similarity transformation with or without scaling.
  
      Imported, and slightly adapted, directly from:
      https://github.com/scikit-image/scikit-image/blob/master/skimage/transform/_geometric.py
  
 
</s> remove         logger.trace("offset: %s", offset)
 </s> add         logger.trace("offset: %s", offset)  # type: ignore </s> remove             logger.trace("center %s: %s", key, center)
 </s> add             logger.trace("center %s: %s", key, center)  # type: ignore </s> remove     def matrix(self):
 </s> add     def matrix(self) -> np.ndarray: </s> remove         if self._matrices[self._centering] is None:
 </s> add         if not np.any(self._matrices[self._centering]): </s> remove     def offset(self):
 </s> add     def offset(self) -> Dict[CenteringType, np.ndarray]:
def _umeyama(source: np.ndarray, destination: np.ndarray, estimate_scale: bool) -> np.ndarray:
          logger.trace("offset: %s", offset)
          return offset
  
  
def _umeyama(source: np.ndarray, destination: np.ndarray, estimate_scale: bool) -> np.ndarray:
      """Estimate N-D similarity transformation with or without scaling.
  
      Imported, and slightly adapted, directly from:
      https://github.com/scikit-image/scikit-image/blob/master/skimage/transform/_geometric.py
  
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