              else:
                  ret.pop(i)
          return tuple(ret)
  
      def call(self, inputs, **kwargs):  # pylint:disable=unused-argument
          """This is where the layer's logic lives.
  
          Override for layers which inherit from this class
  
          Parameters
          ----------
 
</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) </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 class ElementwiseLayerO2K(keras.layers.Layer):
    """ Custom Keras Element Wise layer generated by onnx2keras. """
    def call(self, inputs, **kwargs):  # pylint:disable=unused-argument
        """This is where the layer's logic lives.

        Override for layers that inherit from this class.

        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()

    def compute_output_shape(self, input_shape):  # pylint:disable=no-self-use
        """Computes the output shape of the layer.
 </s> add class L2Norm(keras.layers.Layer):
    """ L2 Normalization layer for S3FD.

    Parameters
    ----------
    n_channels: int
        The number of channels to normalize
    scale: float, optional
        The scaling for initial weights. Default: `1.0`
    """
    def __init__(self, n_channels, scale=1.0, **kwargs):
        super().__init__(**kwargs)
        self._n_channels = n_channels
        self._scale = scale
        self.w = self.add_weight("l2norm",  # pylint:disable=invalid-name
                                 (self._n_channels, ),
                                 trainable=True,
                                 initializer=keras.initializers.Constant(value=self._scale),
                                 dtype="float32") </s> remove class DivO2K(ElementwiseLayerO2K):
    """ Custom Keras Division layer generated by onnx2keras. """
    def call(self, inputs, **kwargs):  # pylint:disable=unused-argument
        """This is where the layer's logic lives.
 </s> add         Normalizes the feed images.
        var_x = self.conv_block(var_x, 256, 3, 3)
        f3_3 = var_x
        var_x = MaxPooling2D(pool_size=2, strides=2)(var_x)
              else:
                  ret.pop(i)
          return tuple(ret)
  
        var_x = self.conv_block(var_x, 256, 3, 3)
        f3_3 = var_x
        var_x = MaxPooling2D(pool_size=2, strides=2)(var_x)
        var_x = self.conv_block(var_x, 256, 3, 3)
        f3_3 = var_x
        var_x = MaxPooling2D(pool_size=2, strides=2)(var_x)
  
          Override for layers which inherit from this class
  
          Parameters
          ----------
keep keep keep keep replace replace keep keep keep keep keep
 extract: S3FD - Convert to re-enable CPU 
plugins/extract/detect/s3fd.py