      """ Custom Keras Sum layer generated by onnx2keras. """
      def call(self, inputs, **kwargs):  # pylint:disable=unused-argument
          """This is where the layer's logic lives.
  
          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
          """
          return K.sum(inputs, self._axes, self._keepdims)
  
  
  class SqrtO2K(keras.layers.Layer):  # pylint:disable=too-few-public-methods
      """ Custom Keras Square Root layer generated by onnx2keras. """
      def call(self, inputs, **kwargs):  # pylint:disable=unused-argument,no-self-use
 
</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
        """
        return K.sqrt(inputs)
 </s> add         f5_3 = ZeroPadding2D(1)(f5_3)
        cls3 = Conv2D(2, kernel_size=3, strides=1, name="conv5_3_norm_mbox_conf")(f5_3)
        reg3 = Conv2D(4, kernel_size=3, strides=1, name="conv5_3_norm_mbox_loc")(f5_3) </s> remove class SqrtO2K(keras.layers.Layer):  # pylint:disable=too-few-public-methods
    """ Custom Keras Square Root layer generated by onnx2keras. """
    def call(self, inputs, **kwargs):  # pylint:disable=unused-argument,no-self-use
        """This is where the layer's logic lives.
 </s> add         f4_3 = ZeroPadding2D(1)(f4_3)
        cls2 = Conv2D(2, kernel_size=3, strides=1, name="conv4_3_norm_mbox_conf")(f4_3)
        reg2 = Conv2D(4, kernel_size=3, strides=1, name="conv4_3_norm_mbox_loc")(f4_3) </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
 </s> add  </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
        """
        return K.pow(*inputs)


class ConstantLayerO2K(keras.layers.Layer):
    """ Custom Keras Constant layer generated by onnx2keras. """
    def __init__(self, constant_obj, dtype, **kwargs):
        self._dtype = np.dtype(dtype).name
        self._constant = np.array(constant_obj, dtype=self._dtype)
        super().__init__(**kwargs)
 </s> add         f7_2 = ZeroPadding2D(1)(f7_2)
        cls6 = Conv2D(2, kernel_size=3, strides=1, name="conv7_2_mbox_conf")(f7_2)
        reg6 = Conv2D(4, kernel_size=3, strides=1, name="conv7_2_mbox_loc")(f7_2) </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)
        f3_3 = L2Norm(256, scale=10, name="conv3_3_norm")(f3_3)
        f4_3 = L2Norm(512, scale=8, name="conv4_3_norm")(f4_3)
        f5_3 = L2Norm(512, scale=5, name="conv5_3_norm")(f5_3)
      """ Custom Keras Sum layer generated by onnx2keras. """
      def call(self, inputs, **kwargs):  # pylint:disable=unused-argument
          """This is where the layer's logic lives.
  
        f3_3 = L2Norm(256, scale=10, name="conv3_3_norm")(f3_3)
        f4_3 = L2Norm(512, scale=8, name="conv4_3_norm")(f4_3)
        f5_3 = L2Norm(512, scale=5, name="conv5_3_norm")(f5_3)
        f3_3 = L2Norm(256, scale=10, name="conv3_3_norm")(f3_3)
        f4_3 = L2Norm(512, scale=8, name="conv4_3_norm")(f4_3)
        f5_3 = L2Norm(512, scale=5, name="conv5_3_norm")(f5_3)
        f3_3 = L2Norm(256, scale=10, name="conv3_3_norm")(f3_3)
        f4_3 = L2Norm(512, scale=8, name="conv4_3_norm")(f4_3)
        f5_3 = L2Norm(512, scale=5, name="conv5_3_norm")(f5_3)
        f3_3 = L2Norm(256, scale=10, name="conv3_3_norm")(f3_3)
        f4_3 = L2Norm(512, scale=8, name="conv4_3_norm")(f4_3)
        f5_3 = L2Norm(512, scale=5, name="conv5_3_norm")(f5_3)
        f3_3 = L2Norm(256, scale=10, name="conv3_3_norm")(f3_3)
        f4_3 = L2Norm(512, scale=8, name="conv4_3_norm")(f4_3)
        f5_3 = L2Norm(512, scale=5, name="conv5_3_norm")(f5_3)
        f3_3 = L2Norm(256, scale=10, name="conv3_3_norm")(f3_3)
        f4_3 = L2Norm(512, scale=8, name="conv4_3_norm")(f4_3)
        f5_3 = L2Norm(512, scale=5, name="conv5_3_norm")(f5_3)
        f3_3 = L2Norm(256, scale=10, name="conv3_3_norm")(f3_3)
        f4_3 = L2Norm(512, scale=8, name="conv4_3_norm")(f4_3)
        f5_3 = L2Norm(512, scale=5, name="conv5_3_norm")(f5_3)
        f3_3 = L2Norm(256, scale=10, name="conv3_3_norm")(f3_3)
        f4_3 = L2Norm(512, scale=8, name="conv4_3_norm")(f4_3)
        f5_3 = L2Norm(512, scale=5, name="conv5_3_norm")(f5_3)
        f3_3 = L2Norm(256, scale=10, name="conv3_3_norm")(f3_3)
        f4_3 = L2Norm(512, scale=8, name="conv4_3_norm")(f4_3)
        f5_3 = L2Norm(512, scale=5, name="conv5_3_norm")(f5_3)
        f3_3 = L2Norm(256, scale=10, name="conv3_3_norm")(f3_3)
        f4_3 = L2Norm(512, scale=8, name="conv4_3_norm")(f4_3)
        f5_3 = L2Norm(512, scale=5, name="conv5_3_norm")(f5_3)
        f3_3 = L2Norm(256, scale=10, name="conv3_3_norm")(f3_3)
        f4_3 = L2Norm(512, scale=8, name="conv4_3_norm")(f4_3)
        f5_3 = L2Norm(512, scale=5, name="conv5_3_norm")(f5_3)
        f3_3 = L2Norm(256, scale=10, name="conv3_3_norm")(f3_3)
        f4_3 = L2Norm(512, scale=8, name="conv4_3_norm")(f4_3)
        f5_3 = L2Norm(512, scale=5, name="conv5_3_norm")(f5_3)
  
  
  class SqrtO2K(keras.layers.Layer):  # pylint:disable=too-few-public-methods
      """ Custom Keras Square Root layer generated by onnx2keras. """
      def call(self, inputs, **kwargs):  # pylint:disable=unused-argument,no-self-use
keep keep keep keep replace replace replace replace replace replace replace replace replace replace replace replace keep keep keep keep keep
 extract: S3FD - Convert to re-enable CPU 
plugins/extract/detect/s3fd.py