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,
        kernel): mean, var = tf.nn.moments(kernel, axes=[0, 1, 2],

because it // is not ACVP tested. func GHASH(key *[16]byte, inputs ...[]byte) []byte { fips140.RecordNonApproved() var out [gcmBlockSize]byte ghash(&out, key, inputs...) return out[:] } // ghash is a variable-time generic implementation of GHASH, which shouldn't // be used on any architecture with hardware support for AES-GCM. // // Each input is zero-padded to 128-bit before being absorbed. func ghash(out, H *[gcmBlockSize]byte, inputs ...[]byte) { // productTable contains the first sixteen powers...

because it // is not ACVP tested. func GHASH(key *[16]byte, inputs ...[]byte) []byte { fips140.RecordNonApproved() var out [gcmBlockSize]byte ghash(&out, key, inputs...) return out[:] } // ghash is a variable-time generic implementation of GHASH, which shouldn't // be used on any architecture with hardware support for AES-GCM. // // Each input is zero-padded to 128-bit before being absorbed. func ghash(out, H *[gcmBlockSize]byte, inputs ...[]byte) { // productTable contains the first sixteen powers...