          return new_face
  
      def _get_image_mask(self,
                          new_face: np.ndarray,
                          detected_face: "DetectedFace",
                          predicted_mask: Optional[np.ndarray],
                          reference_face: "AlignedFace") -> Tuple[np.ndarray, np.ndarray]:
          """ Return any selected image mask
  
          Places the requested mask into the new face's Alpha channel.
  
          Parameters
 
</s> remove             centering: Literal["legacy", "face", "head"],
            predicted_mask: Optional[np.ndarray] = None) -> Tuple[np.ndarray, np.ndarray]:
 </s> add             centering: T.Literal["legacy", "face", "head"],
            predicted_mask: np.ndarray | None = None) -> tuple[np.ndarray, np.ndarray]: </s> remove                   predicted_mask: Optional[np.ndarray],
                  centering: Literal["legacy", "face", "head"],
 </s> add                   predicted_mask: np.ndarray | None,
                  centering: T.Literal["legacy", "face", "head"], </s> remove                               detected_face: "DetectedFace",
                              reference_face: "AlignedFace",
                              predicted_mask: Optional[np.ndarray]) -> np.ndarray:
 </s> add                               detected_face: DetectedFace,
                              reference_face: AlignedFace,
                              predicted_mask: np.ndarray | None) -> np.ndarray: </s> remove                          centering: Literal["legacy", "face", "head"],
 </s> add                          centering: T.Literal["legacy", "face", "head"], </s> remove                        predicted: "ConvertItem",
                       frame_size: Tuple[int, int]) -> Tuple[np.ndarray, np.ndarray]:
 </s> add                        predicted: ConvertItem,
                       frame_size: tuple[int, int]) -> tuple[np.ndarray, np.ndarray]:
                        detected_face: DetectedFace,
                        predicted_mask: np.ndarray | None,
                        reference_face: AlignedFace) -> tuple[np.ndarray, np.ndarray]:
          return new_face
  
      def _get_image_mask(self,
                          new_face: np.ndarray,
                        detected_face: DetectedFace,
                        predicted_mask: np.ndarray | None,
                        reference_face: AlignedFace) -> tuple[np.ndarray, np.ndarray]:
                        detected_face: DetectedFace,
                        predicted_mask: np.ndarray | None,
                        reference_face: AlignedFace) -> tuple[np.ndarray, np.ndarray]:
                        detected_face: DetectedFace,
                        predicted_mask: np.ndarray | None,
                        reference_face: AlignedFace) -> tuple[np.ndarray, np.ndarray]:
          """ Return any selected image mask
  
          Places the requested mask into the new face's Alpha channel.
  
          Parameters
keep keep keep keep replace replace 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 
lib/convert.py