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],
        keepdims=True) # Author code uses std + 1e-5 return
        super().convolution_op(inputs, (kernel - mean) / tf.sqrt(var + 1e-10))`

tables[tableIndex].Select(t, windowValue) p.Add(p, t) tableIndex-- windowValue = byte & 0b1111 tables[tableIndex].Select(t, windowValue) p.Add(p, t) tableIndex-- } return p, nil } // {{.p}}Sqrt sets e to a square root of x. If x is not a square, {{.p}}Sqrt returns // false and e is unchanged. e and x can overlap. func {{.p}}Sqrt(e, x *{{ .Element }}) (isSquare bool) { candidate := new({{ .Element }}) {{.p}}SqrtCandidate(candidate, x) square := new({{ .Element }}).Square(candidate) if square.Equal(x) != 1 {...

tables[tableIndex].Select(t, windowValue) p.Add(p, t) tableIndex-- windowValue = byte & 0b1111 tables[tableIndex].Select(t, windowValue) p.Add(p, t) tableIndex-- } return p, nil } // {{.p}}Sqrt sets e to a square root of x. If x is not a square, {{.p}}Sqrt returns // false and e is unchanged. e and x can overlap. func {{.p}}Sqrt(e, x *{{ .Element }}) (isSquare bool) { candidate := new({{ .Element }}) {{.p}}SqrtCandidate(candidate, x) square := new({{ .Element }}).Square(candidate) if square.Equal(x) != 1 {...