          -------
          :class:`keras.models.Model`
              The compiled SqueezeNet model
          """
          inputs = Input(self._input_shape)
          var_x = Conv2D(64, 3, strides=2, activation="relu", name="features.0")(inputs)
  
          block_idx = 2
          squeeze = 16
          expand = 64
          for idx in range(4):
 
</s> remove             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)
 </s> add             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) </s> remove         :class:`plaidml.tile.Value` or :class:`tf.Tensor`
 </s> add         :class:`tf.Tensor` </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 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_liae")
 </s> add         return KModel(input_, var_x, name="encoder_liae")
        inputs = layers.Input(self._input_shape)
        var_x = layers.Conv2D(64, 3, strides=2, activation="relu", name="features.0")(inputs)
          -------
          :class:`keras.models.Model`
              The compiled SqueezeNet model
          """
        inputs = layers.Input(self._input_shape)
        var_x = layers.Conv2D(64, 3, strides=2, activation="relu", name="features.0")(inputs)
        inputs = layers.Input(self._input_shape)
        var_x = layers.Conv2D(64, 3, strides=2, activation="relu", name="features.0")(inputs)
  
          block_idx = 2
          squeeze = 16
          expand = 64
          for idx in range(4):
keep keep keep keep 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