                  logger.verbose("found face(s) by rotating image %s "  # type:ignore[attr-defined]
                                 "degrees",
                                 angle)
  
              found_faces = cast(List[np.ndarray], ([face if not found.any() else found
                                                     for face, found in zip(batch.prediction,
                                                                            found_faces)]))
  
              if all(face.any() for face in found_faces):
                  logger.trace("Faces found for all images")  # type:ignore[attr-defined]
                  break
  
 
</s> remove         if found != 3:  # Full version could not be determined
            return
 </s> add         if found != 3:  # Full version not determined
            return False
 </s> remove         found_faces: List[np.ndarray] = [np.array([]) for _ in range(len(batch.feed))]
 </s> add         found_faces: list[np.ndarray] = [np.array([]) for _ in range(len(batch.feed))] </s> remove             faces: list[Optional[str]] = [None for _ in range(len(frames))]
 </s> add             faces: list[str | None] = [None for _ in range(len(frames))] </s> remove         retval: List[str] = []
 </s> add         retval: list[str] = [] </s> remove             return
 </s> add             return False

            found_faces = T.cast(list[np.ndarray], ([face if not found.any() else found
                                                     for face, found in zip(batch.prediction,
                                                                            found_faces)]))
                  logger.verbose("found face(s) by rotating image %s "  # type:ignore[attr-defined]
                                 "degrees",
                                 angle)
  
            found_faces = T.cast(list[np.ndarray], ([face if not found.any() else found
                                                     for face, found in zip(batch.prediction,
                                                                            found_faces)]))
            found_faces = T.cast(list[np.ndarray], ([face if not found.any() else found
                                                     for face, found in zip(batch.prediction,
                                                                            found_faces)]))
            found_faces = T.cast(list[np.ndarray], ([face if not found.any() else found
                                                     for face, found in zip(batch.prediction,
                                                                            found_faces)]))
  
              if all(face.any() for face in found_faces):
                  logger.trace("Faces found for all images")  # type:ignore[attr-defined]
                  break
  
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 
plugins/extract/detect/_base.py