      def __init__(self,
                   filters: int,
                   kernel_size: Union[int, Tuple[int, int]] = 3,
                   padding: str = "same",
                   activation: Optional[str] = "leakyrelu",
                   size: int = 2,
                   interpolation: str = "bilinear",
                   **kwargs) -> None:
          self._name = _get_name(f"upscale_dny_{filters}")
          self._interpolation = interpolation
 
</s> remove                  activation: Optional[str] = "leakyrelu",
 </s> add                  activation: T.Optional[str] = "leakyrelu", </s> remove                  normalization: Optional[str] = None,
                 activation: Optional[str] = "leakyrelu",
 </s> add                  normalization: T.Optional[str] = None,
                 activation: T.Optional[str] = "leakyrelu", </s> remove                        activation: Optional[str] = None,
                       upsamples: Optional[int] = None,
                       interpolation: Optional[str] = None) -> keras.layers.Layer:
 </s> add                        activation: T.Optional[str] = None,
                       upsamples: T.Optional[int] = None,
                       interpolation: T.Optional[str] = None) -> keras.layers.Layer: </s> remove     no_amd: bool = False
    tf_min: Tuple[int, int] = (2, 0)
    scaling: Tuple[int, int] = (0, 1)
 </s> add     tf_min: T.Tuple[int, int] = (2, 0)
    scaling: T.Tuple[int, int] = (0, 1) </s> remove         kwargs: Dict[str, Union[str, int]] = {}
 </s> add         kwargs: T.Dict[str, T.Union[str, int]] = {}
                 activation: T.Optional[str] = "leakyrelu",
      def __init__(self,
                   filters: int,
                   kernel_size: Union[int, Tuple[int, int]] = 3,
                   padding: str = "same",
                 activation: T.Optional[str] = "leakyrelu",
                   size: int = 2,
                   interpolation: str = "bilinear",
                   **kwargs) -> None:
          self._name = _get_name(f"upscale_dny_{filters}")
          self._interpolation = interpolation
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 
lib/model/nn_blocks.py