              retval.append(face)
          return retval, sub_folders
  
      def filtered_mask(self,
                        batch: "AlignerBatch",
                        skip: Optional[Union[np.ndarray, List[int]]] = None) -> np.ndarray:
          """ Obtain a list of boolean values for the given batch indicating whether they pass the
          filter test.
  
          Parameters
          ----------
 
</s> remove                                  subbatches: List[AlignerBatch],
                                 original_masks: Optional[np.ndarray] = None) -> np.ndarray:
 </s> add                                  subbatches: list[AlignerBatch],
                                 original_masks: np.ndarray | None = None) -> np.ndarray: </s> remove     def finalize_predictions(self, bounding_boxes_scales: T.List[np.ndarray]) -> np.ndarray:
 </s> add     def finalize_predictions(self, bounding_boxes_scales: list[np.ndarray]) -> np.ndarray: </s> remove     def _compile_feed_faces(feed_faces: T.List[AlignedFace]) -> np.ndarray:
 </s> add     def _compile_feed_faces(feed_faces: list[AlignedFace]) -> np.ndarray: </s> remove     def get_loss(self, session_id: Optional[int]) -> Dict[str, np.ndarray]:
 </s> add     def get_loss(self, session_id: int | None) -> dict[str, np.ndarray]: </s> remove     def _re_insert_filtered(self, batch: "AlignerBatch", masks: np.ndarray) -> np.ndarray:
 </s> add     def _re_insert_filtered(self, batch: AlignerBatch, masks: np.ndarray) -> np.ndarray:
                      batch: AlignerBatch,
                      skip: np.ndarray | list[int] | None = None) -> np.ndarray:
              retval.append(face)
          return retval, sub_folders
  
      def filtered_mask(self,
                      batch: AlignerBatch,
                      skip: np.ndarray | list[int] | None = None) -> np.ndarray:
                      batch: AlignerBatch,
                      skip: np.ndarray | list[int] | None = None) -> np.ndarray:
          """ Obtain a list of boolean values for the given batch indicating whether they pass the
          filter test.
  
          Parameters
          ----------
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