                  continue
              should_filter.append(self._get_matches(f_type, identities))
  
          # If any of the filter or nfilter evaluate to 'should filter' then filter out face
          final_filter: T.List[bool] = np.array(should_filter).max(axis=0).tolist()
          logger.trace("should_filter: %s, final_filter: %s",  # type: ignore
                       should_filter, final_filter)
          return self._filter_faces(faces, sub_folders, final_filter)
  
      def output_counts(self):
 
</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"]): </s> remove     def _process_batch(self, filenames: List[str]) -> BatchType:
 </s> add     def _process_batch(self, filenames: list[str]) -> BatchType: </s> remove     def _filter_small_faces(self, detected_faces: List[List[DetectedFace]]
                            ) -> List[List[DetectedFace]]:
 </s> add     def _filter_small_faces(self, detected_faces: list[list[DetectedFace]]
                            ) -> list[list[DetectedFace]]: </s> remove     def _get_detected_faces(self, filename: str, image: np.ndarray) -> T.List[DetectedFace]:
 </s> add     def _get_detected_faces(self, filename: str, image: np.ndarray) -> list[DetectedFace]: </s> remove                          face: "DetectedFace",
                         faces: List["DetectedFace"],
                         sub_folders: List[Optional[str]],
 </s> add                          face: DetectedFace,
                         faces: list[DetectedFace],
                         sub_folders: list[str | None],
        final_filter: list[bool] = np.array(should_filter).max(axis=0).tolist()
                  continue
              should_filter.append(self._get_matches(f_type, identities))
  
          # If any of the filter or nfilter evaluate to 'should filter' then filter out face
        final_filter: list[bool] = np.array(should_filter).max(axis=0).tolist()
          logger.trace("should_filter: %s, final_filter: %s",  # type: ignore
                       should_filter, final_filter)
          return self._filter_faces(faces, sub_folders, final_filter)
  
      def output_counts(self):
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/recognition/_base.py