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],

// Copyright 2022 The Go Authors. All rights reserved. // Use of this source code is governed by a BSD-style // license that can be found in the LICENSE file. //go:build !purego // Based on the Linux Kernel with the following comment: // Algorithm based on https://git.kernel.org/pub/scm/linux/kernel/git/torvalds/linux.git/commit/?id=fb87127bcefc17efab75 // Originally written by Ard Biesheuvel <******@****.***> #include "textflag.h" #define SHA512TRANS(i0, i1, i2, i3, i4, rc0, in0) \ VADD...

// Copyright 2022 The Go Authors. All rights reserved. // Use of this source code is governed by a BSD-style // license that can be found in the LICENSE file. //go:build !purego // Based on the Linux Kernel with the following comment: // Algorithm based on https://git.kernel.org/pub/scm/linux/kernel/git/torvalds/linux.git/commit/?id=fb87127bcefc17efab75 // Originally written by Ard Biesheuvel <******@****.***> #include "textflag.h" #define SHA512TRANS(i0, i1, i2, i3, i4, rc0, in0) \ VADD...