              The tensor with any normalization applied
          """
          if not self._config["dec_norm"]:
              return inputs
          norms = dict(batch=BatchNormalization,
                       group=GroupNormalization,
                       instance=InstanceNormalization,
                       layer=LayerNormalization,
                       rms=RMSNormalization)
          return norms[self._config["dec_norm"]]()(inputs)
  
      def _dny_entry(self, inputs: Tensor) -> Tensor:
          """ Entry convolutions for using the upscale_dny method.
  
 
</s> remove     norms = dict(layer=LayerNormalization,
                 rms=RMSNormalization,
                 instance=InstanceNormalization)
    bottlenecks = dict(average_pooling=GlobalAveragePooling2D(),
                       dense=Dense(size),
                       max_pooling=GlobalMaxPooling2D())
 </s> add     norms = {"layer": LayerNormalization,
             "rms": RMSNormalization,
             "instance": InstanceNormalization}
    bottlenecks = {"average_pooling": GlobalAveragePooling2D(),
                   "dense": Dense(size),
                   "max_pooling": GlobalMaxPooling2D()} </s> remove     def __init__(self, level: int, scale: float, crop: Tuple[int, int]):
 </s> add     def __init__(self, level: int, scale: float, crop: T.Tuple[int, int]): </s> remove         if get_backend() == "amd":
            return inputs * K.sigmoid(inputs * self.beta)
        # Native TF Implementation has more memory-efficient gradients
 </s> add  </s> remove         inputs: :class:`plaidml.tile.Value` or :class:`tf.Tensor`
 </s> add         inputs: :class:`tf.Tensor` </s> remove         return mixedprecision.LossScaleOptimizer(optimizer)
 </s> add         return mixedprecision.LossScaleOptimizer(optimizer)  # pylint:disable=no-member
        norms = {"batch": BatchNormalization,
                 "group": GroupNormalization,
                 "instance": InstanceNormalization,
                 "layer": LayerNormalization,
                 "rms": RMSNormalization}
              The tensor with any normalization applied
          """
          if not self._config["dec_norm"]:
              return inputs
        norms = {"batch": BatchNormalization,
                 "group": GroupNormalization,
                 "instance": InstanceNormalization,
                 "layer": LayerNormalization,
                 "rms": RMSNormalization}
        norms = {"batch": BatchNormalization,
                 "group": GroupNormalization,
                 "instance": InstanceNormalization,
                 "layer": LayerNormalization,
                 "rms": RMSNormalization}
        norms = {"batch": BatchNormalization,
                 "group": GroupNormalization,
                 "instance": InstanceNormalization,
                 "layer": LayerNormalization,
                 "rms": RMSNormalization}
        norms = {"batch": BatchNormalization,
                 "group": GroupNormalization,
                 "instance": InstanceNormalization,
                 "layer": LayerNormalization,
                 "rms": RMSNormalization}
        norms = {"batch": BatchNormalization,
                 "group": GroupNormalization,
                 "instance": InstanceNormalization,
                 "layer": LayerNormalization,
                 "rms": RMSNormalization}
          return norms[self._config["dec_norm"]]()(inputs)
  
      def _dny_entry(self, inputs: Tensor) -> Tensor:
          """ Entry convolutions for using the upscale_dny method.
  
keep keep keep keep replace replace replace replace 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/phaze_a.py