          f5_3 = var_x
          var_x = MaxPooling2D(pool_size=2, strides=2)(var_x)
  
  
          f6_2 = self.conv_up(var_x, 256, 6)
          f7_2 = self.conv_up(f6_2, 128, 7)
  
 
</s> remove class SumO2K(ReduceLayerO2K):
    """ Custom Keras Sum layer generated by onnx2keras. """
    def call(self, inputs, **kwargs):  # pylint:disable=unused-argument
        """This is where the layer's logic lives.
 </s> add         f6_2 = self.conv_up(var_x, 256, 6)
        f7_2 = self.conv_up(f6_2, 128, 7) </s> remove     def call(self, inputs, **kwargs):  # pylint:disable=unused-argument
        """This is where the layer's logic lives.
 </s> add         var_x = self.conv_block(var_x, 256, 3, 3)
        f3_3 = var_x
        var_x = MaxPooling2D(pool_size=2, strides=2)(var_x) </s> remove         Override for layers which inherit from this class
 </s> add         var_x = self.conv_block(var_x, 512, 4, 3)
        f4_3 = var_x
        var_x = MaxPooling2D(pool_size=2, strides=2)(var_x) </s> remove         Parameters
        ----------
        inputs: Input tensor, or list/tuple of input tensors.
            The input to the layer
        **kwargs: Additional keyword arguments.
            Required for parent class but unused
        Returns
        -------
        A tensor or list/tuple of tensors.
            The layer output
        """
        raise NotImplementedError()
 </s> add         var_x = self.conv_block(var_x, 512, 5, 3)
        f5_3 = var_x
        var_x = MaxPooling2D(pool_size=2, strides=2)(var_x) </s> remove         Returns
        -------
        tuple
            An output shape tuple.
        """
        if self._axes is None:
            return (1,)*len(input_shape) if self._keepdims else tuple()
        ret = list(input_shape)
        for i in sorted(self._axes, reverse=True):
            if self._keepdims:
                ret[i] = 1
            else:
                ret.pop(i)
        return tuple(ret)
 </s> add         var_x = self.conv_block(var_x, 128, 2, 2)
        var_x = MaxPooling2D(pool_size=2, strides=2)(var_x)
        var_x = ZeroPadding2D(3)(var_x)
        var_x = Conv2D(1024, kernel_size=3, strides=1, activation="relu", name="fc6")(var_x)
        var_x = Conv2D(1024, kernel_size=1, strides=1, activation="relu", name="fc7")(var_x)
        ffc7 = var_x
          f5_3 = var_x
          var_x = MaxPooling2D(pool_size=2, strides=2)(var_x)
  
        var_x = ZeroPadding2D(3)(var_x)
        var_x = Conv2D(1024, kernel_size=3, strides=1, activation="relu", name="fc6")(var_x)
        var_x = Conv2D(1024, kernel_size=1, strides=1, activation="relu", name="fc7")(var_x)
        ffc7 = var_x
  
          f6_2 = self.conv_up(var_x, 256, 6)
          f7_2 = self.conv_up(f6_2, 128, 7)
  
keep keep add keep keep keep keep
 extract: S3FD - Convert to re-enable CPU 
plugins/extract/detect/s3fd.py