          masks = np.broadcast_to(np.reshape(masks, (*landmarks.shape[:2], 1, 1)),
                                  landmarks.shape)
          return np.ma.array(landmarks, mask=masks).mean(axis=0).data.astype("float32")
  
      def _process_output_first_pass(self, subbatches: List[AlignerBatch]) -> Tuple[np.ndarray,
                                                                                    np.ndarray]:
          """ Process the output from the aligner if this is the first or only pass.
  
          Parameters
          ----------
 
</s> remove                                     subbatches: List[AlignerBatch],
 </s> add                                     subbatches: list[AlignerBatch], </s> remove     def process_output(self, subbatches: List["AlignerBatch"], batch_masks: np.ndarray) -> None:
 </s> add     def process_output(self, subbatches: list[AlignerBatch], batch_masks: np.ndarray) -> None: </s> remove                           ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
 </s> add                           ) -> tuple[np.ndarray, np.ndarray, np.ndarray]: </s> remove                                angle: int) -> Tuple[np.ndarray, np.ndarray]:
 </s> add                                angle: int) -> tuple[np.ndarray, np.ndarray]: </s> remove     def finalize_predictions(self, bounding_boxes_scales: T.List[np.ndarray]) -> np.ndarray:
 </s> add     def finalize_predictions(self, bounding_boxes_scales: list[np.ndarray]) -> np.ndarray:
    def _process_output_first_pass(self, subbatches: list[AlignerBatch]) -> tuple[np.ndarray,
          masks = np.broadcast_to(np.reshape(masks, (*landmarks.shape[:2], 1, 1)),
                                  landmarks.shape)
          return np.ma.array(landmarks, mask=masks).mean(axis=0).data.astype("float32")
  
    def _process_output_first_pass(self, subbatches: list[AlignerBatch]) -> tuple[np.ndarray,
                                                                                    np.ndarray]:
          """ Process the output from the aligner if this is the first or only pass.
  
          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/align/_base/aligner.py