      def _predict(self, batch: BatchType) -> DetectorBatch:
          """ Wrap models predict function in rotations """
          assert isinstance(batch, DetectorBatch)
          batch.rotation_matrix = [np.array([]) for _ in range(len(batch.feed))]
          found_faces: List[np.ndarray] = [np.array([]) for _ in range(len(batch.feed))]
          for angle in self.rotation:
              # Rotate the batch and insert placeholders for already found faces
              self._rotate_batch(batch, angle)
              try:
                  pred = self.predict(batch.feed)
 
</s> remove         self._embeddings: List[np.ndarray] = [np.array([]) for _ in self._filter_files]
        self._nembeddings: List[np.ndarray] = [np.array([]) for _ in self._nfilter_files]
 </s> add         self._embeddings: list[np.ndarray] = [np.array([]) for _ in self._filter_files]
        self._nembeddings: list[np.ndarray] = [np.array([]) for _ in self._nfilter_files] </s> remove         batch.feed = np.array([cast(np.ndarray, feed.face)[..., :3]
 </s> add         batch.feed = np.array([T.cast(np.ndarray, feed.face)[..., :3] </s> remove         batch.feed = np.array([cast(np.ndarray, feed.face)[..., :3]
 </s> add         batch.feed = np.array([T.cast(np.ndarray, feed.face)[..., :3] </s> remove         retval: List[np.ndarray] = []
        filtered_landmarks: List[np.ndarray] = []
 </s> add         retval: list[np.ndarray] = []
        filtered_landmarks: list[np.ndarray] = [] </s> remove         retval = dict(alignments_file=alignments,
                      faces_dir=[None for _ in range(len(alignments))],
                      frames_dir=[None for _ in range(len(alignments))])
 </s> add         retval = {"alignments_file": alignments,
                  "faces_dir": [None for _ in range(len(alignments))],
                  "frames_dir": [None for _ in range(len(alignments))]}
        found_faces: list[np.ndarray] = [np.array([]) for _ in range(len(batch.feed))]
      def _predict(self, batch: BatchType) -> DetectorBatch:
          """ Wrap models predict function in rotations """
          assert isinstance(batch, DetectorBatch)
          batch.rotation_matrix = [np.array([]) for _ in range(len(batch.feed))]
        found_faces: list[np.ndarray] = [np.array([]) for _ in range(len(batch.feed))]
          for angle in self.rotation:
              # Rotate the batch and insert placeholders for already found faces
              self._rotate_batch(batch, angle)
              try:
                  pred = self.predict(batch.feed)
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/detect/_base.py