          dict
              The configuration for the layer
          """
          config = super().get_config()
          config.update({
              'axes': self._axes,
              'keepdims': self._keepdims
          })
          return config
  
      def compute_output_shape(self, input_shape):
          """Computes the output shape of the layer.
  
 
</s> remove     def compute_output_shape(self, input_shape):
        """Computes the output shape of the layer.
 </s> add  </s> remove     def get_config(self):
        """ Returns the config of the layer.

        A layer config is a Python dictionary (serializable) containing the configuration of a
        layer. The same layer can be re-instantiated later (without its trained weights) from this
        configuration. The config of a layer does not include connectivity information, nor the
        layer class name. These are handled by `Network` (one layer of abstraction above).

        Returns
        -------
        dict
            The configuration for the layer
        """
        config = super().get_config()
        config.update({
            'starts': self._starts, 'ends': self._ends,
            'axes': self._axes, 'steps': self._steps
        })
        return config

 </s> add  </s> remove         dict
            The configuration for the layer
 </s> add         tensor
            The output tensor from the convolution block </s> remove         config = super().get_config()
        config.update({
            'constant_obj': self._constant,
            'dtype': self._dtype
        })
        return config
 </s> add         name = "conv{}".format(idx)
        var_x = inputs
        for i in range(1, 3):
            rec_name = "{}_{}".format(name, i)
            size = 1 if i == 1 else 3
            if i == 2:
                var_x = ZeroPadding2D(1, name="{}.zeropad".format(rec_name))(var_x)
            var_x = Conv2D(filters * i,
                           kernel_size=size,
                           strides=i,
                           activation="relu",
                           name=rec_name)(var_x)
        return var_x </s> remove         A layer config is a Python dictionary (serializable) containing the configuration of a
        layer. The same layer can be re-instantiated later (without its trained weights) from this
        configuration. The config of a layer does not include connectivity information, nor the
        layer class name. These are handled by `Network` (one layer of abstraction above).

 </s> add 
        config.update({"starts": self._starts,
                       "ends": self._ends,
                       "axes": self._axes,
                       "steps": self._steps})
          dict
              The configuration for the layer
          """
          config = super().get_config()
        config.update({"starts": self._starts,
                       "ends": self._ends,
                       "axes": self._axes,
                       "steps": self._steps})
        config.update({"starts": self._starts,
                       "ends": self._ends,
                       "axes": self._axes,
                       "steps": self._steps})
        config.update({"starts": self._starts,
                       "ends": self._ends,
                       "axes": self._axes,
                       "steps": self._steps})
        config.update({"starts": self._starts,
                       "ends": self._ends,
                       "axes": self._axes,
                       "steps": self._steps})
          return config
  
      def compute_output_shape(self, input_shape):
          """Computes the output shape of the layer.
  
keep keep keep keep replace replace replace replace keep keep keep keep keep
 extract: S3FD - Convert to re-enable CPU 
plugins/extract/detect/s3fd.py