pkg math, func Erfcinv(float64) float64
pkg math, func Erfinv(float64) float64
pkg math, func Round(float64) float64
pkg math, func RoundToEven(float64) float64
pkg math/big, const MaxBase = 62
pkg math/big, method (*Float) Sqrt(*Float) *Float
pkg math/big, method (*Int) CmpAbs(*Int) int
pkg math/rand, func Shuffle(int, func(int, int))
pkg math/rand, method (*Rand) Shuffle(int, func(int, int))

        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))`

the non-blank method and field names must be distinct.
</p>

<p>
Given defined type <code>Point</code>, the declarations
</p>

<pre>
func (p *Point) Length() float64 {
	return math.Sqrt(p.x * p.x + p.y * p.y)
}

func (p *Point) Scale(factor float64) {
	p.x *= factor
	p.y *= factor
}
</pre>

<p>
bind the methods <code>Length</code> and <code>Scale</code>,

the non-blank method and field names must be distinct.
</p>

<p>
Given defined type <code>Point</code> the declarations
</p>

<pre>
func (p *Point) Length() float64 {
	return math.Sqrt(p.x * p.x + p.y * p.y)
}

func (p *Point) Scale(factor float64) {
	p.x *= factor
	p.y *= factor
}
</pre>

<p>
bind the methods <code>Length</code> and <code>Scale</code>,

pkg math, func Signbit(float64) bool
pkg math, func Sin(float64) float64
pkg math, func Sincos(float64) (float64, float64)
pkg math, func Sinh(float64) float64
pkg math, func Sqrt(float64) float64
pkg math, func Tan(float64) float64
pkg math, func Tanh(float64) float64
pkg math, func Trunc(float64) float64
pkg math, func Y0(float64) float64
pkg math, func Y1(float64) float64