          logger.debug("Compiled Samples: %s", {k: [i.shape for i in v] for k, v in retval.items()})
          return retval
  
      def set_timelapse_feed(self,
                             images: T.Dict[Literal["a", "b"], T.List[str]],
                             batch_size: int) -> None:
          """ Set the time-lapse feed for this feeder.
  
          Creates a generator from :class:`lib.training_data.PreviewDataGenerator` specifically
          for generating time-lapse previews for the feeder.
 
</s> remove     def _set_preview_feed(self) -> T.Dict[Literal["a", "b"], T.Generator[BatchType, None, None]]:
 </s> add     def _set_preview_feed(self) -> dict[T.Literal["a", "b"], Generator[BatchType, None, None]]: </s> remove         retval: T.Dict[Literal["a", "b"], T.List[np.ndarray]] = {}
        for side in get_args(Literal["a", "b"]):
 </s> add         retval: dict[T.Literal["a", "b"], list[np.ndarray]] = {}
        for side in T.get_args(T.Literal["a", "b"]): </s> remove                  images: T.Dict[Literal["a", "b"], T.List[str]],
 </s> add                  images: dict[T.Literal["a", "b"], list[str]], </s> remove                  config: T.Dict[str, ConfigValueType]) -> None:
 </s> add                  config: dict[str, ConfigValueType]) -> None: </s> remove                         batch_size: T.Optional[int] = None,
                        images: T.Optional[T.List[str]] = None) -> DataGenerator:
 </s> add                         batch_size: int | None = None,
                        images: list[str] | None = None) -> DataGenerator:
                           images: dict[T.Literal["a", "b"], list[str]],
          logger.debug("Compiled Samples: %s", {k: [i.shape for i in v] for k, v in retval.items()})
          return retval
  
      def set_timelapse_feed(self,
                           images: dict[T.Literal["a", "b"], list[str]],
                             batch_size: int) -> None:
          """ Set the time-lapse feed for this feeder.
  
          Creates a generator from :class:`lib.training_data.PreviewDataGenerator` specifically
          for generating time-lapse previews for the feeder.
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/train/trainer/_base.py