          """
          data = K.constant(self._constant, dtype=self._dtype)
          return data
  
      def compute_output_shape(self, input_shape):  # pylint:disable=unused-argument
          """Computes the output shape of the layer.
  
          Assumes that the layer will be built to match that input shape provided.
  
          Parameters
          ----------
 
</s> remove         Parameters
        ----------
        inputs: Input tensor, or list/tuple of input tensors.
            The input to the layer. Required for parent class but unused
        **kwargs: Additional keyword arguments.
            Required for parent class but unused
        Returns
        -------
        A tensor or list/tuple of tensors.
            The layer output
        """
        data = K.constant(self._constant, dtype=self._dtype)
        return data
 </s> add         bmax = Maximum()([chunks[0], chunks[1], chunks[2]])
        cls1 = Concatenate()([bmax, chunks[3]]) </s> remove     def compute_output_shape(self, input_shape):
        """Computes the output shape of the layer.
 </s> add  </s> remove         Assumes that the layer will be built to match that input shape provided.
 </s> add     def call(self, inputs):
        """ Call the L2 Normalization Layer. </s> remove         Assumes that the layer will be built to match that input shape provided.
 </s> add     @classmethod
    def conv_block(cls, inputs, filters, idx, recursions):
        """ First round convolutions with zero padding added. </s> remove         Assumes that the layer will be built to match that input shape provided.
 </s> add class S3fd(KSession):
    """ Keras Network """
    def __init__(self, model_path, model_kwargs, allow_growth, exclude_gpus, confidence):
        logger.debug("Initializing: %s: (model_path: '%s', model_kwargs: %s, allow_growth: %s, "
                     "exclude_gpus: %s, confidence: %s)", self.__class__.__name__, model_path,
                     model_kwargs, allow_growth, exclude_gpus, confidence)
        super().__init__("S3FD",
                         model_path,
                         model_kwargs=model_kwargs,
                         allow_growth=allow_growth,
                         exclude_gpus=exclude_gpus)
        self.define_model(self.model_definition)
        self.load_model_weights()
        self.confidence = confidence
        self.average_img = np.array([104.0, 117.0, 123.0])
        logger.debug("Initialized: %s", self.__class__.__name__)
        return [input_], [cls1, reg1, cls2, reg2, cls3, reg3, cls4, reg4, cls5, reg5, cls6, reg6]
          """
          data = K.constant(self._constant, dtype=self._dtype)
          return data
  
        return [input_], [cls1, reg1, cls2, reg2, cls3, reg3, cls4, reg4, cls5, reg5, cls6, reg6]
        return [input_], [cls1, reg1, cls2, reg2, cls3, reg3, cls4, reg4, cls5, reg5, cls6, reg6]
  
          Assumes that the layer will be built to match that input shape provided.
  
          Parameters
          ----------
keep keep keep keep replace replace keep keep keep keep keep
 extract: S3FD - Convert to re-enable CPU 
plugins/extract/detect/s3fd.py