          :class:`numpy.ndarry`
              A compiled preview image ready for display or saving
          """
          logger.debug("Showing sample")
          feeds: T.Dict[Literal["a", "b"], np.ndarray] = {}
          for idx, side in enumerate(get_args(Literal["a", "b"])):
              feed = self.images[side][0]
              input_shape = self._model.model.input_shape[idx][1:]
              if input_shape[0] / feed.shape[1] != 1.0:
                  feeds[side] = self._resize_sample(side, feed, input_shape[0])
              else:
 
</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         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         retval: T.Dict[Literal["a", "b"], T.Generator[BatchType, None, None]] = {}
 </s> add         retval: 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"]):
        feeds: dict[T.Literal["a", "b"], np.ndarray] = {}
        for idx, side in enumerate(T.get_args(T.Literal["a", "b"])):
          :class:`numpy.ndarry`
              A compiled preview image ready for display or saving
          """
          logger.debug("Showing sample")
        feeds: dict[T.Literal["a", "b"], np.ndarray] = {}
        for idx, side in enumerate(T.get_args(T.Literal["a", "b"])):
        feeds: dict[T.Literal["a", "b"], np.ndarray] = {}
        for idx, side in enumerate(T.get_args(T.Literal["a", "b"])):
              feed = self.images[side][0]
              input_shape = self._model.model.input_shape[idx][1:]
              if input_shape[0] / feed.shape[1] != 1.0:
                  feeds[side] = self._resize_sample(side, feed, input_shape[0])
              else:
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