          """
          name = f"features.{block_idx}"
          var_x = inputs
          if max_pool:
              var_x = MaxPool2D(pool_size=3, strides=2, name=f"{name}.pool")(var_x)
          var_x = ZeroPadding2D(padding=padding, name=f"{name}.pad")(var_x)
          var_x = Conv2D(filters,
                         kernel_size=kernel_size,
                         strides=strides,
                         padding="valid",
                         activation="relu",
                         name=name)(var_x)
          return var_x
  
      def __call__(self) -> Model:
          """ Create the AlexNet Model
  
 
</s> remove         inputs = Input(self._input_shape)
        var_x = Conv2D(64, 3, strides=2, activation="relu", name="features.0")(inputs)
 </s> add         inputs = layers.Input(self._input_shape)
        var_x = layers.Conv2D(64, 3, strides=2, activation="relu", name="features.0")(inputs) </s> remove     def model_definition() -> Tuple[List[Tensor], List[Tensor]]:
 </s> add     def model_definition() -> T.Tuple[T.List[Tensor], T.List[Tensor]]: </s> remove         return KerasModel(input_, var_x, name="encoder")
 </s> add         return KModel(input_, var_x, name="encoder") </s> remove         return KerasModel(input_, var_x, name="encoder")
 </s> add         return KModel(input_, var_x, name="encoder")
 </s> remove         return KerasModel(input_, var_x, name="encoder")
 </s> add         return KModel(input_, var_x, name="encoder")
            var_x = layers.MaxPool2D(pool_size=3, strides=2, name=f"{name}.pool")(var_x)
        var_x = layers.ZeroPadding2D(padding=padding, name=f"{name}.pad")(var_x)
        var_x = layers.Conv2D(filters,
                              kernel_size=kernel_size,
                              strides=strides,
                              padding="valid",
                              activation="relu",
                              name=name)(var_x)
          """
          name = f"features.{block_idx}"
          var_x = inputs
          if max_pool:
            var_x = layers.MaxPool2D(pool_size=3, strides=2, name=f"{name}.pool")(var_x)
        var_x = layers.ZeroPadding2D(padding=padding, name=f"{name}.pad")(var_x)
        var_x = layers.Conv2D(filters,
                              kernel_size=kernel_size,
                              strides=strides,
                              padding="valid",
                              activation="relu",
                              name=name)(var_x)
            var_x = layers.MaxPool2D(pool_size=3, strides=2, name=f"{name}.pool")(var_x)
        var_x = layers.ZeroPadding2D(padding=padding, name=f"{name}.pad")(var_x)
        var_x = layers.Conv2D(filters,
                              kernel_size=kernel_size,
                              strides=strides,
                              padding="valid",
                              activation="relu",
                              name=name)(var_x)
            var_x = layers.MaxPool2D(pool_size=3, strides=2, name=f"{name}.pool")(var_x)
        var_x = layers.ZeroPadding2D(padding=padding, name=f"{name}.pad")(var_x)
        var_x = layers.Conv2D(filters,
                              kernel_size=kernel_size,
                              strides=strides,
                              padding="valid",
                              activation="relu",
                              name=name)(var_x)
            var_x = layers.MaxPool2D(pool_size=3, strides=2, name=f"{name}.pool")(var_x)
        var_x = layers.ZeroPadding2D(padding=padding, name=f"{name}.pad")(var_x)
        var_x = layers.Conv2D(filters,
                              kernel_size=kernel_size,
                              strides=strides,
                              padding="valid",
                              activation="relu",
                              name=name)(var_x)
            var_x = layers.MaxPool2D(pool_size=3, strides=2, name=f"{name}.pool")(var_x)
        var_x = layers.ZeroPadding2D(padding=padding, name=f"{name}.pad")(var_x)
        var_x = layers.Conv2D(filters,
                              kernel_size=kernel_size,
                              strides=strides,
                              padding="valid",
                              activation="relu",
                              name=name)(var_x)
            var_x = layers.MaxPool2D(pool_size=3, strides=2, name=f"{name}.pool")(var_x)
        var_x = layers.ZeroPadding2D(padding=padding, name=f"{name}.pad")(var_x)
        var_x = layers.Conv2D(filters,
                              kernel_size=kernel_size,
                              strides=strides,
                              padding="valid",
                              activation="relu",
                              name=name)(var_x)
            var_x = layers.MaxPool2D(pool_size=3, strides=2, name=f"{name}.pool")(var_x)
        var_x = layers.ZeroPadding2D(padding=padding, name=f"{name}.pad")(var_x)
        var_x = layers.Conv2D(filters,
                              kernel_size=kernel_size,
                              strides=strides,
                              padding="valid",
                              activation="relu",
                              name=name)(var_x)
            var_x = layers.MaxPool2D(pool_size=3, strides=2, name=f"{name}.pool")(var_x)
        var_x = layers.ZeroPadding2D(padding=padding, name=f"{name}.pad")(var_x)
        var_x = layers.Conv2D(filters,
                              kernel_size=kernel_size,
                              strides=strides,
                              padding="valid",
                              activation="relu",
                              name=name)(var_x)
          return var_x
  
      def __call__(self) -> Model:
          """ Create the AlexNet Model
  
keep keep keep keep replace replace replace 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 
lib/model/nets.py