              self._face_cache.pre_fill(images, side)
          self._processing = ImageAugmentation(batch_size,
                                               self._process_size,
                                               self._config)
          self._nearest_landmarks: Dict[str, Tuple[str, ...]] = {}
          logger.debug("Initialized %s", self.__class__.__name__)
  
      def _create_targets(self, batch: np.ndarray) -> List[np.ndarray]:
          """ Compile target images, with masks, for the model output sizes.
  
 
</s> remove     def _create_targets(self, batch: np.ndarray) -> List[np.ndarray]:
 </s> add     def _create_targets(self, batch: np.ndarray) -> list[np.ndarray]: </s> remove     def get_summary_stats(self) -> List[dict]:
 </s> add     def get_summary_stats(self) -> list[dict]: </s> remove         self.queues: Dict[str, EventQueue] = {}
 </s> add         self.queues: dict[str, EventQueue] = {} </s> remove         self._face_cache: "_Cache" = get_cache(side,
                                               filenames=images,
                                               config=self._config,
                                               size=self._process_size,
                                               coverage_ratio=self._coverage_ratio)
 </s> add         self._face_cache: _Cache = get_cache(side,
                                             filenames=images,
                                             config=self._config,
                                             size=self._process_size,
                                             coverage_ratio=self._coverage_ratio) </s> remove         self._time_stats: Dict[int, Dict[str, Union[float, int]]] = {}
        self._per_session_stats: List[Dict[str, Any]] = []
 </s> add         self._time_stats: dict[int, dict[str, float | int]] = {}
        self._per_session_stats: list[dict[str, T.Any]] = []
        self._nearest_landmarks: dict[str, tuple[str, ...]] = {}
              self._face_cache.pre_fill(images, side)
          self._processing = ImageAugmentation(batch_size,
                                               self._process_size,
                                               self._config)
        self._nearest_landmarks: dict[str, tuple[str, ...]] = {}
          logger.debug("Initialized %s", self.__class__.__name__)
  
      def _create_targets(self, batch: np.ndarray) -> List[np.ndarray]:
          """ Compile target images, with masks, for the model output sizes.
  
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 
lib/training/generator.py