                              "nadam": (optimizers.Nadam,
                                        {"beta_1": 0.5, "beta_2": 0.99, "epsilon": epsilon}),
                              "rms-prop": (optimizers.RMSprop, {"epsilon": epsilon})}
          optimizer_info = valid_optimizers[optimizer]
          self._optimizer: T.Callable = optimizer_info[0]
          self._kwargs: T.Dict[str, T.Any] = optimizer_info[1]
  
          self._configure(learning_rate, autoclip)
          logger.verbose("Using %s optimizer", optimizer.title())  # type:ignore[attr-defined]
          logger.debug("Initialized: %s", self.__class__.__name__)
  
 
</s> remove         self._loss_functions: List[compile_utils.LossesContainer] = []
        self._loss_weights: List[float] = []
        self._mask_channels: List[int] = []
 </s> add         self._loss_functions: list[compile_utils.LossesContainer] = []
        self._loss_weights: list[float] = []
        self._mask_channels: list[int] = [] </s> remove         self._state: Dict[str, Any] = {}
 </s> add         self._state: dict[str, T.Any] = {} </s> remove         self._images: Dict[str, "np.ndarray"] = {}
 </s> add         self._images: dict[str, np.ndarray] = {} </s> remove     kwargs: T.Dict[str, T.Any] = field(default_factory=dict)
 </s> add     kwargs: dict[str, T.Any] = field(default_factory=dict) </s> remove         assert config["centering"] in get_args(CenteringType)
        self._centering: CenteringType = cast(CenteringType, config["centering"])
 </s> add         assert config["centering"] in T.get_args(CenteringType)
        self._centering: CenteringType = T.cast(CenteringType, config["centering"])
        self._optimizer: Callable = optimizer_info[0]
        self._kwargs: dict[str, T.Any] = optimizer_info[1]
                              "nadam": (optimizers.Nadam,
                                        {"beta_1": 0.5, "beta_2": 0.99, "epsilon": epsilon}),
                              "rms-prop": (optimizers.RMSprop, {"epsilon": epsilon})}
          optimizer_info = valid_optimizers[optimizer]
        self._optimizer: Callable = optimizer_info[0]
        self._kwargs: dict[str, T.Any] = optimizer_info[1]
        self._optimizer: Callable = optimizer_info[0]
        self._kwargs: dict[str, T.Any] = optimizer_info[1]
  
          self._configure(learning_rate, autoclip)
          logger.verbose("Using %s optimizer", optimizer.title())  # type:ignore[attr-defined]
          logger.debug("Initialized: %s", self.__class__.__name__)
  
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/model/_base/settings.py