          assert isinstance(batch, AlignerBatch)
          logger.trace("Aligning faces around center")  # type:ignore[attr-defined]
          center_scale = self.get_center_scale(batch.detected_faces)
          batch.feed = np.array(self.crop(batch, center_scale))[..., :3]
          batch.data.append(dict(center_scale=center_scale))
          logger.trace("Aligned image around center")  # type:ignore[attr-defined]
  
      def get_center_scale(self, detected_faces: List["DetectedFace"]) -> np.ndarray:
          """ Get the center and set scale of bounding box
  
 
</s> remove     def get_center_scale(self, detected_faces: List["DetectedFace"]) -> np.ndarray:
 </s> add     def get_center_scale(self, detected_faces: list[DetectedFace]) -> np.ndarray: </s> remove         batch.data.append(dict(roi=roi, offsets=offsets))
 </s> add         batch.data.append({"roi": roi, "offsets": offsets}) </s> remove         batch.feed = np.array([cast(np.ndarray, feed.face)[..., :3]
 </s> add         batch.feed = np.array([T.cast(np.ndarray, feed.face)[..., :3] </s> remove         batch.feed = np.array([cast(np.ndarray, feed.face)[..., :3]
 </s> add         batch.feed = np.array([T.cast(np.ndarray, feed.face)[..., :3] </s> remove             x_center = (cast(int, face.left) + face.right) / 2.0
            y_center = (cast(int, face.top) + face.bottom) / 2.0 - cast(int, face.height) * 0.12
            scale = (cast(int, face.width) + cast(int, face.height)) * self.reference_scale
            center_scale[index, :, 0] = np.full(68, x_center, dtype='float32')
            center_scale[index, :, 1] = np.full(68, y_center, dtype='float32')
 </s> add             x_ctr = (T.cast(int, face.left) + face.right) / 2.0
            y_ctr = (T.cast(int, face.top) + face.bottom) / 2.0 - T.cast(int, face.height) * 0.12
            scale = (T.cast(int, face.width) + T.cast(int, face.height)) * self.reference_scale
            center_scale[index, :, 0] = np.full(68, x_ctr, dtype='float32')
            center_scale[index, :, 1] = np.full(68, y_ctr, dtype='float32')
        batch.data.append({"center_scale": center_scale})
          assert isinstance(batch, AlignerBatch)
          logger.trace("Aligning faces around center")  # type:ignore[attr-defined]
          center_scale = self.get_center_scale(batch.detected_faces)
          batch.feed = np.array(self.crop(batch, center_scale))[..., :3]
        batch.data.append({"center_scale": center_scale})
          logger.trace("Aligned image around center")  # type:ignore[attr-defined]
  
      def get_center_scale(self, detected_faces: List["DetectedFace"]) -> np.ndarray:
          """ Get the center and set scale of bounding box
  
keep keep keep keep replace keep keep keep keep keep
 Rebase code (#1326)

* Remove tensorflow_probability requirement

* setup.py - fix progress bars

* requirements.txt: Remove pre python 3.9 packages

* update apple requirements.txt

* update INSTALL.md

* Remove python<3.9 code

* setup.py - fix Windows Installer

* typing: python3.9 compliant

* Update pytest and readthedocs python versions

* typing fixes

* Python Version updates
  - Reduce max version to 3.10
  - Default to 3.10 in installers
  - Remove incompatible 3.11 tests

* Update dependencies

* Downgrade imageio dep for Windows

* typing: merge optional unions and fixes

* Updates
  - min python version 3.10
  - typing to python 3.10 spec
  - remove pre-tf2.10 code
  - Add conda tests

* train: re-enable optimizer saving

* Update dockerfiles

* Update setup.py
  - Apple Conda deps to setup.py
  - Better Cuda + dependency handling

* bugfix: Patch logging to prevent Autograph errors

* Update dockerfiles

* Setup.py - Setup.py - stdout to utf-8

* Add more OSes to github Actions

* suppress mac-os end to end test 
plugins/extract/align/fan.py