  
      @property
      def pose(self):
          """ :class:`lib.align.PoseEstimate`: The estimated pose in 3D space. """
          with self._cache["pose"][1]:
              if self._cache["pose"][0] is None:
                  lms = cv2.transform(np.expand_dims(self._frame_landmarks, axis=1),
                                      self._matrices["legacy"]).squeeze()
                  self._cache["pose"][0] = PoseEstimate(lms)
          return self._cache["pose"][0]
  
 
</s> remove     def _head_size(self):
        """ int: The size of the full head extract image calculated from the required
        centering. """
        with self._cache["head_size"][1]:
            if self._centering not in self._cache["head_size"][0]:
                self._cache["head_size"][0][self._centering] = get_centered_size(self._centering,
                                                                                 "head",
                                                                                 self.size)
        return self._cache["head_size"][0][self._centering]

    @property
    def pose(self):
 </s> add     def pose(self) -> "PoseEstimate": </s> remove                 self._cache["pose"][0] = PoseEstimate(lms)
        return self._cache["pose"][0]
 </s> add                 self._cache.pose = PoseEstimate(lms)
        return self._cache.pose </s> remove     def adjusted_matrix(self):
 </s> add     def adjusted_matrix(self) -> np.ndarray: </s> remove         with self._cache["landmarks"][1]:
            if self._cache["landmarks"][0] is None:
 </s> add         with self._cache.lock("landmarks"):
            if self._cache.landmarks is None: </s> remove         with self._cache["landmarks_normalized"][1]:
            if self._cache["landmarks_normalized"][0] is None:
 </s> add         with self._cache.lock("landmarks_normalized"):
            if self._cache.landmarks_normalized is None:
        with self._cache.lock("pose"):
            if self._cache.pose is None:
  
      @property
      def pose(self):
          """ :class:`lib.align.PoseEstimate`: The estimated pose in 3D space. """
        with self._cache.lock("pose"):
            if self._cache.pose is None:
        with self._cache.lock("pose"):
            if self._cache.pose is None:
                  lms = cv2.transform(np.expand_dims(self._frame_landmarks, axis=1),
                                      self._matrices["legacy"]).squeeze()
                  self._cache["pose"][0] = PoseEstimate(lms)
          return self._cache["pose"][0]
  
keep keep keep keep replace 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