          retval = source_identities @ test_identity / (s_norm * i_norm)
          return retval
  
      def _get_matches(self,
                       filter_type: Literal["filter", "nfilter"],
                       identities: np.ndarray) -> np.ndarray:
          """ Obtain the average and minimum distances for each face against the source identities
          to test against
  
          Parameters
 
</s> remove         retval = batch_size / np.diff(cast(np.ndarray, _SESSION.get_timestamps(self._session_id)))
 </s> add         retval = batch_size / np.diff(T.cast(np.ndarray,
                                             _SESSION.get_timestamps(self._session_id))) </s> remove     def _generate_weights(cls, channel: Dict[str, float], domain: np.ndarray) -> tf.Tensor:
 </s> add     def _generate_weights(cls, channel: dict[str, float], domain: np.ndarray) -> tf.Tensor: </s> remove         retval = self._to_float32(np.array([AlignedFace(face.landmarks_xy,
                                                        image=images[idx],
                                                        centering=cast(CenteringType,
                                                                       self._config["centering"]),
                                                        size=full_size,
                                                        dtype="uint8",
                                                        is_aligned=True).face
                                            for idx, face in enumerate(detected_faces)]))
 </s> add         retval = self._to_float32(np.array([
            AlignedFace(face.landmarks_xy,
                        image=images[idx],
                        centering=T.cast(CenteringType,
                                         self._config["centering"]),
                        size=full_size,
                        dtype="uint8",
                        is_aligned=True).face
            for idx, face in enumerate(detected_faces)])) </s> remove     def _transform_to_frame(self, batch: "AlignerBatch") -> np.ndarray:
 </s> add     def _transform_to_frame(self, batch: AlignerBatch) -> np.ndarray: </s> remove     def _predict(self, feed_faces: np.ndarray, batch_size: T.Optional[int] = None) -> np.ndarray:
 </s> add     def _predict(self, feed_faces: np.ndarray, batch_size: int | None = None) -> np.ndarray:
                     filter_type: T.Literal["filter", "nfilter"],
          retval = source_identities @ test_identity / (s_norm * i_norm)
          return retval
  
      def _get_matches(self,
                     filter_type: T.Literal["filter", "nfilter"],
                       identities: np.ndarray) -> np.ndarray:
          """ Obtain the average and minimum distances for each face against the source identities
          to test against
  
          Parameters
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