const (
		s    = 32
		mask = 1<<s - 1
	)
	u0 := u & mask
	u1 := u >> s
	v0 := v & mask
	v1 := v >> s
	w0 := u0 * v0
	t := u1*v0 + w0>>s
	w1 := t & mask
	w2 := t >> s
	w1 += u0 * v1
	return u * v, u1*v1 + w2 + w1>>s
}

// 128/64 -> 64 quotient, 64 remainder.
// adapted from hacker's delight
func divlu(u1, u0, v uint64) (q, r uint64) {
	const b = 1 << 32

	if u1 >= v {

    readPriority(handler, streamId)
  }

  @Throws(IOException::class)
  private fun readPriority(
    handler: Handler,
    streamId: Int,
  ) {
    val w1 = source.readInt()
    val exclusive = w1 and 0x80000000.toInt() != 0
    val streamDependency = w1 and 0x7fffffff
    val weight = (source.readByte() and 0xff) + 1
    handler.priority(streamId, streamDependency, weight, exclusive)
  }

  @Throws(IOException::class)

	kind := asmKindForType(t, size)
	cc = append(cc, newComponent(suffix, kind, s, off, size, suffix))

	switch kind {
	case 8:
		if arch.ptrSize == 4 {
			w1, w2 := "lo", "hi"
			if arch.bigEndian {
				w1, w2 = w2, w1
			}
			cc = append(cc, newComponent(suffix+"_"+w1, 4, "half "+s, off, 4, suffix))
			cc = append(cc, newComponent(suffix+"_"+w2, 4, "half "+s, off+4, 4, suffix))
		}

	case asmEmptyInterface:

	}
}

// Resolving weak types Rules W1-W7.
//
// Note that some weak types (EN, AN) remain after this processing is
// complete.
func (s *isolatingRunSequence) resolveWeakTypes() {

	// on entry, only these types remain
	s.assertOnly(L, R, AL, EN, ES, ET, AN, CS, B, S, WS, ON, NSM, LRI, RLI, FSI, PDI)

	// Rule W1.
	// Changes all NSMs.
	precedingCharacterType := s.sos

func Mul64(x, y uint64) (hi, lo uint64) {
	const mask32 = 1<<32 - 1
	x0 := x & mask32
	x1 := x >> 32
	y0 := y & mask32
	y1 := y >> 32
	w0 := x0 * y0
	t := x1*y0 + w0>>32
	w1 := t & mask32
	w2 := t >> 32
	w1 += x0 * y1
	hi = x1*y1 + w2 + w1>>32
	lo = x * y
	return
}

// --- Full-width divide ---

// Div returns the quotient and remainder of (hi, lo) divided by y:

// CHECK-DAG: %[[w1:.*]] = arith.constant dense<{{\[\[\[\[}}-1.000000e+00, 1.000000e+00]]]
// CHECK-DAG: %[[b_q:.*]] = "tfl.quantize"(%[[bias]]){{.*}}{7.8740158861230386E-10,1.9998891450408216E-8,1.9998891805679583E-7}
// CHECK-DAG: %[[b_dq:.*]] = "tfl.dequantize"(%[[b_q]])
// CHECK-DAG: %[[w1_q:.*]] = "tfl.quantize"(%[[w1]]){{.*}}{0.0078740157480314959,0.015748031496062992,0.015748031496062992}

  return success();
}

// Convert:
//   %y0 = dot_general(%x0, %w0)
//   %y1 = dot_general(%x1, %w1)
//   ...
//   concatenate(%y0, %y1, ...)
// To:
//   %x = concatenate(%x0, %x1, ...)
//   %w = concatenate(%w0, %w1, ...)
//   dot_general(%x, %w)
//
// To simplify the implementation, we only handle the case where the final
// concat is on the only batching dim.

//      Let z = 1/x, then we approximation
//              f(z) = lgamma(x) - (x-0.5)(log(x)-1)
//      by
//                                  3       5             11
//              w = w0 + w1*z + w2*z  + w3*z  + ... + w6*z
//      where
//              |w - f(z)| < 2**-58.74
//
//   4. For negative x, since (G is gamma function)
//              -x*G(-x)*G(x) = pi/sin(pi*x),
//      we have

00000090  35 35 33 5a 30 41 31 0b  30 09 06 03 55 04 06 13  |553Z0A1.0...U...|
000000a0  02 41 55 31 0c 30 0a 06  03 55 04 08 13 03 4e 53  |.AU1.0...U....NS|
000000b0  57 31 10 30 0e 06 03 55  04 07 13 07 50 79 72 6d  |W1.0...U....Pyrm|
000000c0  6f 6e 74 31 12 30 10 06  03 55 04 03 13 09 4a 6f  |ont1.0...U....Jo|
000000d0  65 6c 20 53 69 6e 67 30  81 9b 30 10 06 07 2a 86  |el Sing0..0...*.|

00000090  35 35 33 5a 30 41 31 0b  30 09 06 03 55 04 06 13  |553Z0A1.0...U...|
000000a0  02 41 55 31 0c 30 0a 06  03 55 04 08 13 03 4e 53  |.AU1.0...U....NS|
000000b0  57 31 10 30 0e 06 03 55  04 07 13 07 50 79 72 6d  |W1.0...U....Pyrm|
000000c0  6f 6e 74 31 12 30 10 06  03 55 04 03 13 09 4a 6f  |ont1.0...U....Jo|
000000d0  65 6c 20 53 69 6e 67 30  81 9b 30 10 06 07 2a 86  |el Sing0..0...*.|