                       "disable_filter: %s)", self.__class__.__name__, detector, aligner, masker,
                       recognition, configfile, multiprocess, exclude_gpus, rotate_images, min_size,
                       normalize_method, re_feed, re_align, disable_filter)
          self._instance = _get_instance()
          maskers = [cast(Optional[str],
                     masker)] if not isinstance(masker, list) else cast(List[Optional[str]], masker)
          self._flow = self._set_flow(detector, aligner, maskers, recognition)
          self._exclude_gpus = exclude_gpus
          # We only ever need 1 item in each queue. This is 2 items cached (1 in queue 1 waiting
          # for queue) at each point. Adding more just stacks RAM with no speed benefit.
          self._queue_size = 1
 
</s> remove             queue = cast("EventQueue", args[0])
 </s> add             queue = T.cast("EventQueue", args[0]) </s> remove         retval: Union[str, List[str]] = paths[0] if len(paths) == 1 else paths
 </s> add         retval: str | list[str] = paths[0] if len(paths) == 1 else paths </s> remove         self._model: Optional[Model] = None
 </s> add         self._model: Model | None = None </s> remove         predicted: T.List[np.ndarray] = inbound if isinstance(inbound, list) else [inbound]
 </s> add         predicted: list[np.ndarray] = inbound if isinstance(inbound, list) else [inbound] </s> remove             idx: Optional[int] = int(sidx)
 </s> add             idx: int | None = int(sidx)
        maskers = [T.cast(str | None,
                   masker)] if not isinstance(masker, list) else T.cast(list[str | None],
                                                                        masker)
                       "disable_filter: %s)", self.__class__.__name__, detector, aligner, masker,
                       recognition, configfile, multiprocess, exclude_gpus, rotate_images, min_size,
                       normalize_method, re_feed, re_align, disable_filter)
          self._instance = _get_instance()
        maskers = [T.cast(str | None,
                   masker)] if not isinstance(masker, list) else T.cast(list[str | None],
                                                                        masker)
        maskers = [T.cast(str | None,
                   masker)] if not isinstance(masker, list) else T.cast(list[str | None],
                                                                        masker)
          self._flow = self._set_flow(detector, aligner, maskers, recognition)
          self._exclude_gpus = exclude_gpus
          # We only ever need 1 item in each queue. This is 2 items cached (1 in queue 1 waiting
          # for queue) at each point. Adding more just stacks RAM with no speed benefit.
          self._queue_size = 1
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/pipeline.py