  
          # MyPy can't recurse into nested dicts to get the type :(
          iterator = T.cast(T.Dict[Literal["a", "b"], T.Generator[BatchType, None, None]],
                            self._display_feeds["timelapse" if is_timelapse else "preview"])
          for side in get_args(Literal["a", "b"]):
              side_feed, side_samples = next(iterator[side])
              batchsizes.append(len(side_samples[0]))
              samples[side] = side_samples[0]
              feed[side] = side_feed[..., :3]
              masks[side] = side_feed[..., 3][..., 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]"], </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         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"]):
        for side in T.get_args(T.Literal["a", "b"]):
  
          # MyPy can't recurse into nested dicts to get the type :(
          iterator = T.cast(T.Dict[Literal["a", "b"], T.Generator[BatchType, None, None]],
                            self._display_feeds["timelapse" if is_timelapse else "preview"])
        for side in T.get_args(T.Literal["a", "b"]):
              side_feed, side_samples = next(iterator[side])
              batchsizes.append(len(side_samples[0]))
              samples[side] = side_samples[0]
              feed[side] = side_feed[..., :3]
              masks[side] = side_feed[..., 3][..., None]
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