              The index of the current block (for standardized naming convention)
  
          Returns
          -------
          :class:`plaidml.tile.Value` or :class:`tf.Tensor`
              The output of the SqueezeNet fire block
          """
          name = f"features.{block_idx}"
          squeezed = Conv2D(squeeze_planes, 1, activation="relu", name=f"{name}.squeeze")(inputs)
          expand1 = Conv2D(expand_planes, 1, activation="relu", name=f"{name}.expand1x1")(squeezed)
 
</s> remove         inputs: :class:`plaidml.tile.Value` or :class:`tf.Tensor`
 </s> add         inputs: :class:`tf.Tensor` </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             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     def __init__(self, input_shape: Optional[Tuple[int, int, int]] = None) -> None:
 </s> add     def __init__(self, input_shape: T.Optional[T.Tuple[int, int, int]] = None) -> None: </s> remove         dim = K.int_shape(image) if self._batch_shape is None else self._batch_shape
 </s> add         dim = K.int_shape(image)
        :class:`tf.Tensor`
              The index of the current block (for standardized naming convention)
  
          Returns
          -------
        :class:`tf.Tensor`
              The output of the SqueezeNet fire block
          """
          name = f"features.{block_idx}"
          squeezed = Conv2D(squeeze_planes, 1, activation="relu", name=f"{name}.squeeze")(inputs)
          expand1 = Conv2D(expand_planes, 1, activation="relu", name=f"{name}.expand1x1")(squeezed)
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/nets.py