              List of UINT8 aligned faces batch for each selected refeed
          """
          logger.trace("Processing batch: %s, landmarks: %s",  # type: ignore[attr-defined]
                       batch.filename, [b.shape for b in batch.landmarks])
          retval: List[np.ndarray] = []
          filtered_landmarks: List[np.ndarray] = []
          for landmarks, masks in zip(batch.landmarks, batch.second_pass_masks):
              if not np.all(masks):  # At least one face has not already been filtered
                  aligned_faces = [AlignedFace(lms,
                                               image=image,
                                               size=self._size,
 
</s> remove         retval: T.List[DetectedFace] = []
 </s> add         retval: list[DetectedFace] = [] </s> remove         sub_folders: List[Optional[str]] = [None for _ in range(len(faces))]
 </s> add         sub_folders: list[str | None] = [None for _ in range(len(faces))] </s> remove         retval: List[AlignerBatch] = []
 </s> add         retval: list[AlignerBatch] = [] </s> remove         faces: List[AlignedFace] = batch.data[0]["aligned_faces"]
 </s> add         faces: list[AlignedFace] = batch.data[0]["aligned_faces"] </s> remove         should_filter: T.List[np.ndarray] = []
        for f_type in get_args(Literal["filter", "nfilter"]):
 </s> add         should_filter: list[np.ndarray] = []
        for f_type in T.get_args(T.Literal["filter", "nfilter"]):
        retval: list[np.ndarray] = []
        filtered_landmarks: list[np.ndarray] = []
              List of UINT8 aligned faces batch for each selected refeed
          """
          logger.trace("Processing batch: %s, landmarks: %s",  # type: ignore[attr-defined]
                       batch.filename, [b.shape for b in batch.landmarks])
        retval: list[np.ndarray] = []
        filtered_landmarks: list[np.ndarray] = []
        retval: list[np.ndarray] = []
        filtered_landmarks: list[np.ndarray] = []
          for landmarks, masks in zip(batch.landmarks, batch.second_pass_masks):
              if not np.all(masks):  # At least one face has not already been filtered
                  aligned_faces = [AlignedFace(lms,
                                               image=image,
                                               size=self._size,
keep keep keep keep 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 
plugins/extract/align/_base/processing.py