          feed = [feed_faces]
          logger.trace("Input shape(s): %s", [item.shape for item in feed])  # type:ignore
  
          inbound = self._model.model.predict(feed, verbose=0, batch_size=batch_size)
          predicted: T.List[np.ndarray] = inbound if isinstance(inbound, list) else [inbound]
  
          if self._model.color_order.lower() == "rgb":
              predicted[0] = predicted[0][..., ::-1]
  
          logger.trace("Output shape(s): %s",  # type:ignore
 
</s> remove         for idx, item in enumerate(cast(List[Dict[str, str]], self._frames.file_list_sorted)):
 </s> add         for idx, item in enumerate(T.cast(list[dict[str, str]], self._frames.file_list_sorted)): </s> remove         compiled_layers: T.Dict[str, tf.keras.layers.Layer] = {}
 </s> add         compiled_layers: dict[str, tf.keras.layers.Layer] = {} </s> remove         self._model: Optional[Model] = None
 </s> add         self._model: Model | None = None </s> remove         event_parse._parse_outputs = cast(MagicMock, mocker.MagicMock())  # type:ignore
        event_parse._process_event = cast(MagicMock, mocker.MagicMock())  # type:ignore
        event_parse._cache.cache_data = cast(MagicMock, mocker.MagicMock())  # type:ignore
 </s> add         event_parse._parse_outputs = T.cast(MagicMock, mocker.MagicMock())  # type:ignore
        event_parse._process_event = T.cast(MagicMock, mocker.MagicMock())  # type:ignore
        event_parse._cache.cache_data = T.cast(MagicMock, mocker.MagicMock())  # type:ignore </s> remove         f_display._faces_from_frames = cast(MagicMock, mocker.MagicMock())  # type:ignore
        f_display._header_text = cast(  # type:ignore
 </s> add         f_display._faces_from_frames = T.cast(MagicMock, mocker.MagicMock())  # type:ignore
        f_display._header_text = T.cast(  # type:ignore
        predicted: list[np.ndarray] = inbound if isinstance(inbound, list) else [inbound]
          feed = [feed_faces]
          logger.trace("Input shape(s): %s", [item.shape for item in feed])  # type:ignore
  
          inbound = self._model.model.predict(feed, verbose=0, batch_size=batch_size)
        predicted: list[np.ndarray] = inbound if isinstance(inbound, list) else [inbound]
  
          if self._model.color_order.lower() == "rgb":
              predicted[0] = predicted[0][..., ::-1]
  
          logger.trace("Output shape(s): %s",  # type:ignore
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 
scripts/convert.py