- message: url must have schema one of [http, https, file, oci]
                  rule: 'isURL(self) ? (url(self).getScheme() in ['''', ''http'',
                    ''https'', ''oci'', ''file'']) : (isURL(''http://'' + self) &&
                    url(''http://'' +self).getScheme() in ['''', ''http'', ''https'',
                    ''oci'', ''file''])'
              verificationKey:

                  rule: '!(has(self.caCertificateRefs) && size(self.caCertificateRefs)
                    > 0 && has(self.wellKnownCACertificates) && self.wellKnownCACertificates
                    != "")'
                - message: must specify either CACertificateRefs or WellKnownCACertificates
                  rule: (has(self.caCertificateRefs) && size(self.caCertificateRefs)

                - message: url must have schema one of [http, https, file, oci]
                  rule: 'isURL(self) ? (url(self).getScheme() in ['''', ''http'',
                    ''https'', ''oci'', ''file'']) : (isURL(''http://'' + self) &&
                    url(''http://'' +self).getScheme() in ['''', ''http'', ''https'',
                    ''oci'', ''file''])'
              verificationKey:

        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