// to the decoded value.
func (z *Float) GobDecode(buf []byte) error {
	if len(buf) == 0 {
		// Other side sent a nil or default value.
		*z = Float{}
		return nil
	}
	if len(buf) < 6 {
		return errors.New("Float.GobDecode: buffer too small")
	}

	if buf[0] != floatGobVersion {
		return fmt.Errorf("Float.GobDecode: encoding version %d not supported", buf[0])
	}

	oldPrec := z.prec

	// The sharp flag has no effect for binary float format.
	{"%#b", 1.0, "4503599627370496p-52"},
	// Precision has no effect for binary float format.
	{"%.4b", float32(1.0), "8388608p-23"},
	{"%.4b", -1.0, "-4503599627370496p-52"},
	// Test correct f.intbuf boundary checks.
	{"%.68f", 1.0, zeroFill("1.", 68, "")},
	{"%.68f", -1.0, zeroFill("-1.", 68, "")},
	// float infinites and NaNs
	{"%f", posInf, "+Inf"},

func initNorm() (testKn []uint32, testWn, testFn []float32) {
	const m1 = 1 << 31
	var (
		dn float64 = rn
		tn         = dn
		vn float64 = 9.91256303526217e-3
	)

	testKn = make([]uint32, 128)
	testWn = make([]float32, 128)
	testFn = make([]float32, 128)

	q := vn / math.Exp(-0.5*dn*dn)
	testKn[0] = uint32((dn / q) * m1)
	testKn[1] = 0
	testWn[0] = float32(q / m1)
	testWn[127] = float32(dn / m1)

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

// Test that nil checks are removed.
// Optimization is enabled.

package p

func f5(p *float32, q *float64, r *float32, s *float64) float64 {
	x := float64(*p) // ERROR "generated nil check"
	y := *q          // ERROR "generated nil check"
	*r = 7           // ERROR "generated nil check"
	*s = 9           // ERROR "generated nil check"
	return x + y

}

func (v *Int) Set(value int64) {
	v.i.Store(value)
}

// Float is a 64-bit float variable that satisfies the [Var] interface.
type Float struct {
	f atomic.Uint64
}

func (v *Float) Value() float64 {
	return math.Float64frombits(v.f.Load())
}

func (v *Float) String() string {
	return string(v.appendJSON(nil))
}

func (v *Float) appendJSON(b []byte) []byte {

      // If the tensor is a weight, it should have type FLOAT16.
      // If the tensor is a bias, it should have type FLOAT16.
      // If the tensor is an input or output it should have type FLOAT32.
      // The input to dequantize should be FLOAT16, and all other tensors should
      // be FLOAT32.
      if (i == dequant_input_idx) {
        EXPECT_EQ(quant_tensor->type(), TensorType_FLOAT16);

	{"complex", `_ = complex(1, 0)`, `invalid type`}, // constant
	{"complex", `var re float32; _ = complex(re, 1.0)`, `func(float32, float32) complex64`},
	{"complex", `var im float64; _ = complex(1, im)`, `func(float64, float64) complex128`},
	{"complex", `type F32 float32; var re, im F32; _ = complex(re, im)`, `func(p.F32, p.F32) complex64`},
	{"complex", `type F64 float64; var re, im F64; _ = complex(re, im)`, `func(p.F64, p.F64) complex128`},

		i := u >> 32 & 0x7F
		x := float64(j) * float64(wn[i])
		if absInt32(j) < kn[i] {
			// This case should be hit better than 99% of the time.
			return x
		}

		if i == 0 {
			// This extra work is only required for the base strip.
			for {
				x = -math.Log(r.Float64()) * (1.0 / rn)
				y := -math.Log(r.Float64())
				if y+y >= x*x {
					break
				}
			}
			if j > 0 {
				return rn + x
			}
			return -rn - x

            "type": "integer",
            "format": "int64"
          },
          "float32": {
            "description": "Float32 field.",
            "type": "number",
            "format": "float32"
          },
          "float64": {
            "description": "Float64 field.",
            "type": "number",
            "format": "float64"
          }
        },
        "x-kubernetes-group-version-kind": [
          {

		},
		{
			name: "float64",
			obj:  float64(2.71),
		},
		{
			name: "float64 max",
			obj:  float64(math.MaxFloat64),
		},
		{
			name: "float64 smallest nonzero",
			obj:  float64(math.SmallestNonzeroFloat64),
		},
		{
			name: "float64 no fractional component",
			obj:  float64(5),
		},
		{
			name: "float32",
			obj:  float32(2.71),
		},
		{