          filename = f"{model_name}_state.{self._serializer.file_extension}"
          self._filename = os.path.join(model_dir, filename)
          self._name = model_name
          self._iterations = 0
          self._mixed_precision_layers: List[str] = []
          self._rebuild_model = False
          self._sessions: Dict[int, dict] = {}
          self._lowest_avg_loss: Dict[str, float] = {}
          self._config: Dict[str, "ConfigValueType"] = {}
          self._load(config_changeable_items)
 
</s> remove         self._sessions: Dict[int, dict] = {}
        self._lowest_avg_loss: Dict[str, float] = {}
        self._config: Dict[str, "ConfigValueType"] = {}
 </s> add         self._sessions: T.Dict[int, dict] = {}
        self._lowest_avg_loss: T.Dict[str, float] = {}
        self._config: T.Dict[str, ConfigValueType] = {} </s> remove         self._display = dict(min=show_min, mean=show_mean, max=show_max)
 </s> add         self._display = {"min": show_min, "mean": show_mean, "max": show_max} </s> remove         batchsizes: List[int] = []
        feed: Dict[Literal["a", "b"], np.ndarray] = {}
        samples: Dict[Literal["a", "b"], np.ndarray] = {}
        masks: Dict[Literal["a", "b"], np.ndarray] = {}
 </s> add         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> remove         kwargs: Dict[str, Union[str, int]] = {}
 </s> add         kwargs: T.Dict[str, T.Union[str, int]] = {} </s> remove if TYPE_CHECKING:
 </s> add if T.TYPE_CHECKING:
        self._mixed_precision_layers: T.List[str] = []
          filename = f"{model_name}_state.{self._serializer.file_extension}"
          self._filename = os.path.join(model_dir, filename)
          self._name = model_name
          self._iterations = 0
        self._mixed_precision_layers: T.List[str] = []
          self._rebuild_model = False
          self._sessions: Dict[int, dict] = {}
          self._lowest_avg_loss: Dict[str, float] = {}
          self._config: Dict[str, "ConfigValueType"] = {}
          self._load(config_changeable_items)
keep keep keep keep replace keep keep keep keep keep
 Remove plaidML support (#1325)

* Remove PlaidML reference from readme files

* Remove AMD option from installers

* remove amd requirements and update setup.py

* remove plaidml test from CI workflow

* gpustats: remove plaidml backend

* plaid removals:
  - faceswap.py - python version check
  - setup.cfg - plaidml typing ignore
  - lib.keras_utils - All plaid code
  - lib.launcher.py - All plaidml checks and configuration

* remove tf2.2 specific code from GUI event reader

* lib.model - remove all plaidml implementations

* plugins.extract - remove plaidml code

* plugins.train remove plaidml code

* lib.convert - remove plaidml code

* tools.model: remove plaidml code

* Remove plaidML tests from unit tests

* remove plaidml_utils and docsting cleanups

* Remove plaidML refs from configs

* fix keras imports 
plugins/train/model/_base/model.py