const LargeB = LargeA * LargeA * LargeA
const LargeC = LargeB * LargeB * LargeB // GC_ERROR "constant multiplication overflow"

const AlsoLargeA = LargeA << 400 << 400 >> 400 >> 400 // GC_ERROR "constant shift overflow"

// Issue #42732.

const a = 1e+500000000
const b = a * a // ERROR "constant multiplication overflow|not representable"
const c = b * b

const MaxInt512 = (1<<256 - 1) * (1<<256 + 1)

	return time.Duration(res.NsPerOp())
}

func computeKaratsubaThresholds() {
	fmt.Printf("Multiplication times for varying Karatsuba thresholds\n")
	fmt.Printf("(run repeatedly for good results)\n")

	// determine Tk, the work load execution time using basic multiplication
	Tb := measureKaratsuba(1e9) // th == 1e9 => Karatsuba multiplication disabled
	fmt.Printf("Tb = %10s\n", Tb)

	// thresholds
	th := 4
	th1 := -1
	th2 := -1

// the compiler into (c+d)*n + d*k (with c+d and d*k computed at
// compile time).
//
// The merging is performed by a combination of the multiplication
// merge rules
//  (c*n + d*n) -> (c+d)*n
// and the distributive multiplication rules
//  c * (d+x)  ->  c*d + c*x

// Generate a MergeTest that looks like this:
//
//   a8, b8 = m1*n8 + m2*(n8+k), (m1+m2)*n8 + m2*k
//   if a8 != b8 {

func feMulGeneric(v, a, b *Element) {
	a0 := a.l0
	a1 := a.l1
	a2 := a.l2
	a3 := a.l3
	a4 := a.l4

	b0 := b.l0
	b1 := b.l1
	b2 := b.l2
	b3 := b.l3
	b4 := b.l4

	// Limb multiplication works like pen-and-paper columnar multiplication, but
	// with 51-bit limbs instead of digits.
	//
	//                          a4   a3   a2   a1   a0  x
	//                          b4   b3   b2   b1   b0  =

// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.

//go:build (amd64 || arm64) && !purego

package nistec

import "errors"

// Montgomery multiplication modulo org(G). Sets res = in1 * in2 * R⁻¹.
//
//go:noescape
func p256OrdMul(res, in1, in2 *p256OrdElement)

// Montgomery square modulo org(G), repeated n times (n >= 1).
//
//go:noescape

	fmt.Printf("var failed = false\n")

	f1 := testMul(17, 32)
	f2 := testMul(131, 64)

	fmt.Printf("func main() {\n")
	fmt.Println(f1)
	fmt.Println(f2)
	fmt.Printf("if failed {\n	panic(\"multiplication failed\")\n}\n")
	fmt.Printf("}\n")

			h1, c = bits.Add64(h1, binary.LittleEndian.Uint64(buf[8:16]), c)
			h2 += c

			msg = nil
		}

		// Multiplication of big number limbs is similar to elementary school
		// columnar multiplication. Instead of digits, there are 64-bit limbs.
		//
		// We are multiplying a 3 limbs number, h, by a 2 limbs number, r.
		//
		//                        h2    h1    h0  x

// license that can be found in the LICENSE file.

package test

import "testing"

// Benchmark multiplication of an integer by various constants.
//
// The comment above each sub-benchmark provides an example of how the
// target multiplication operation might be implemented using shift
// (multiplication by a power of 2), addition and subtraction
// operations. It is platform-dependent whether these transformations

	c := a * b
	return c, c/b == a
}

// int64MultiplyScale10 multiplies a by 10, or returns false if that would overflow. This method is faster than
// int64Multiply(a, 10) because the compiler can optimize constant factor multiplication.
func int64MultiplyScale10(a int64) (int64, bool) {
	if a == 0 || a == 1 {
		return a * 10, true
	}
	if a == mostNegative {
		return 0, false
	}
	c := a * 10
	return c, c/10 == a
}

    %4:2 = tf_executor.island wraps "tf.Mul"(%2#1, %2#1) {T = "tfdtype$DT_INT32"} : (tensor<i32>, tensor<i32>) -> tensor<i32> loc("Multiplication")
    %5:3 = tf_executor.Merge %3#0, %4#0 : tensor<i32> {device = "", N = 2, T = "tfdtype$DT_INT32"} loc("Merge")
    tf_executor.fetch
  }
  func.return
}