          list:
              List of :class:`numpy.ndarray` of predictions received from the model
          """
          logger.debug("Getting Predictions")
          preds: T.Dict[str, np.ndarray] = {}
          standard = self._model.model.predict([feed_a, feed_b], verbose=0)
          swapped = self._model.model.predict([feed_b, feed_a], verbose=0)
  
          if self._model.config["learn_mask"]:  # Add mask to 4th channel of final output
              standard = [np.concatenate(side[-2:], axis=-1) for side in standard]
 
</s> remove         masks3: T.Union[T.List[np.ndarray], np.ndarray] = []
 </s> add         masks3: list[np.ndarray] | np.ndarray = [] </s> remove         model_inputs: T.List[T.List[np.ndarray]] = []
        model_targets: T.List[T.List[np.ndarray]] = []
 </s> add         model_inputs: list[list[np.ndarray]] = []
        model_targets: list[list[np.ndarray]] = [] </s> remove             side = cast(Literal["a", "b"], side)
 </s> add             side = T.cast(T.Literal["a", "b"], side) </s> remove         retval: Dict[int, np.ndarray] = {}
 </s> add         retval: dict[int, 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] = {}
        preds: dict[str, np.ndarray] = {}
          list:
              List of :class:`numpy.ndarray` of predictions received from the model
          """
          logger.debug("Getting Predictions")
        preds: dict[str, np.ndarray] = {}
          standard = self._model.model.predict([feed_a, feed_b], verbose=0)
          swapped = self._model.model.predict([feed_b, feed_a], verbose=0)
  
          if self._model.config["learn_mask"]:  # Add mask to 4th channel of final output
              standard = [np.concatenate(side[-2:], axis=-1) for side in standard]
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