              # Set the initial batch so we always rotate from zero
              batch.initial_feed = batch.feed.copy()
              return
  
          feeds: List[np.ndarray] = []
          rotmats: List[np.ndarray] = []
          for img, faces, rotmat in zip(batch.initial_feed,
                                        batch.prediction,
                                        batch.rotation_matrix):
              if faces.any():
                  image = np.zeros_like(img)
 
</s> remove         retval: List[np.ndarray] = []
        filtered_landmarks: List[np.ndarray] = []
 </s> add         retval: list[np.ndarray] = []
        filtered_landmarks: list[np.ndarray] = [] </s> remove         self.source: List[ConvertItem] = []  # Source images, filenames + detected faces
 </s> add         self.source: list[ConvertItem] = []  # Source images, filenames + detected faces </s> remove         self.destination: List[np.ndarray] = []  # Swapped + patched images
 </s> add         self.destination: list[np.ndarray] = []  # Swapped + patched images </s> remove     detected_faces: List[List["DetectedFace"]] = field(default_factory=list)
    rotation_matrix: List[np.ndarray] = field(default_factory=list)
    scale: List[float] = field(default_factory=list)
    pad: List[Tuple[int, int]] = field(default_factory=list)
 </s> add     detected_faces: list[list["DetectedFace"]] = field(default_factory=list)
    rotation_matrix: list[np.ndarray] = field(default_factory=list)
    scale: list[float] = field(default_factory=list)
    pad: list[tuple[int, int]] = field(default_factory=list) </s> remove         samples: List[np.ndarray] = []
 </s> add         samples: list[np.ndarray] = []
        feeds: list[np.ndarray] = []
        rotmats: list[np.ndarray] = []
              # Set the initial batch so we always rotate from zero
              batch.initial_feed = batch.feed.copy()
              return
  
        feeds: list[np.ndarray] = []
        rotmats: list[np.ndarray] = []
        feeds: list[np.ndarray] = []
        rotmats: list[np.ndarray] = []
          for img, faces, rotmat in zip(batch.initial_feed,
                                        batch.prediction,
                                        batch.rotation_matrix):
              if faces.any():
                  image = np.zeros_like(img)
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/detect/_base.py