StandardizedConv2D(tf.keras.layers.Conv2D): def call(self, inputs):
        mean, var = tf.nn.moments(self.kernel, axes=[0, 1, 2], keepdims=True)
        return self.convolution_op(inputs, (self.kernel - mean) / tf.sqrt(var +
        1e-10))` Alternatively, you can override `convolution_op`: `python class
        StandardizedConv2D(tf.keras.Layer): def convolution_op(self, inputs,

}

def TF_SelfAdjointEigV2Op : TF_Op<"SelfAdjointEigV2", [Pure]> {
  let summary = [{
Computes the eigen decomposition of one or more square self-adjoint matrices.
  }];

  let description = [{
Computes the eigenvalues and (optionally) eigenvectors of each inner matrix in