          self._output_file = output
          logger.debug("Time-lapse output set to '%s'", self._output_file)
  
          # Rewrite paths to pull from the training images so mask and face data can be accessed
          images: T.Dict[Literal["a", "b"], T.List[str]] = {}
          for side, input_ in zip(get_args(Literal["a", "b"]), (input_a, input_b)):
              training_path = os.path.dirname(self._image_paths[side][0])
              images[side] = [os.path.join(training_path, os.path.basename(pth))
                              for pth in get_image_paths(input_)]
  
          batchsize = min(len(images["a"]),
 
</s> remove         figures: T.Dict[Literal["a", "b"], np.ndarray] = {}
        headers: T.Dict[Literal["a", "b"], np.ndarray] = {}
 </s> add         figures: dict[T.Literal["a", "b"], np.ndarray] = {}
        headers: dict[T.Literal["a", "b"], np.ndarray] = {} </s> remove         batchsizes: T.List[int] = []
        feed: T.Dict[Literal["a", "b"], np.ndarray] = {}
        samples: T.Dict[Literal["a", "b"], np.ndarray] = {}
        masks: T.Dict[Literal["a", "b"], np.ndarray] = {}
 </s> add         batchsizes: list[int] = []
        feed: dict[T.Literal["a", "b"], np.ndarray] = {}
        samples: dict[T.Literal["a", "b"], np.ndarray] = {}
        masks: dict[T.Literal["a", "b"], np.ndarray] = {} </s> remove         for side in get_args(Literal["a", "b"]):
 </s> add         for side in T.get_args(T.Literal["a", "b"]): </s> remove         iterator = T.cast(T.Dict[Literal["a", "b"], T.Generator[BatchType, None, None]],
 </s> add         iterator = T.cast(dict[T.Literal["a", "b"], "Generator[BatchType, None, None]"], </s> remove         iterator = T.cast(T.Dict[Literal["a", "b"], T.Generator[BatchType, None, None]],
 </s> add         iterator = T.cast(dict[T.Literal["a", "b"], "Generator[BatchType, None, None]"],
        images: dict[T.Literal["a", "b"], list[str]] = {}
        for side, input_ in zip(T.get_args(T.Literal["a", "b"]), (input_a, input_b)):
          self._output_file = output
          logger.debug("Time-lapse output set to '%s'", self._output_file)
  
          # Rewrite paths to pull from the training images so mask and face data can be accessed
        images: dict[T.Literal["a", "b"], list[str]] = {}
        for side, input_ in zip(T.get_args(T.Literal["a", "b"]), (input_a, input_b)):
        images: dict[T.Literal["a", "b"], list[str]] = {}
        for side, input_ in zip(T.get_args(T.Literal["a", "b"]), (input_a, input_b)):
              training_path = os.path.dirname(self._image_paths[side][0])
              images[side] = [os.path.join(training_path, os.path.basename(pth))
                              for pth in get_image_paths(input_)]
  
          batchsize = min(len(images["a"]),
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/train/trainer/_base.py