b.Run(fmt.Sprintf("Size%d", size), func(b *testing.B) {
			floats := make([]float64, size)
			for i := range floats {
				floats[i] = float64(i)
			}
			midpoint := len(floats) / 2
			needle := (floats[midpoint] + floats[midpoint+1]) / 2
			b.ResetTimer()
			for i := 0; i < b.N; i++ {
				slices.BinarySearch(floats, needle)
			}
		})
	}
}

type myStruct struct {
	a, b, c, d string

		{name: "FCVTSD", argLength: 1, reg: fp11, asm: "FCVTSD", typ: "Float32"},                                                            // float32(arg0)
		{name: "FMOVDload", argLength: 2, reg: fpload, asm: "MOVD", aux: "SymOff", typ: "Float64", faultOnNilArg0: true, symEffect: "Read"}, // load float64 from arg0+auxint+aux

// 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

      // 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);

pkg log/slog, func SetLogLoggerLevel(Level) Level #62418
pkg math/big, method (*Rat) FloatPrec() (int, bool) #50489
pkg math/rand/v2, func ExpFloat64() float64 #61716
pkg math/rand/v2, func Float32() float32 #61716
pkg math/rand/v2, func Float64() float64 #61716
pkg math/rand/v2, func Int() int #61716
pkg math/rand/v2, func Int32() int32 #61716
pkg math/rand/v2, func Int32N(int32) int32 #61716

				vars[idx] = escaper + "<binary>" + escaper
			}
		case int, int8, int16, int32, int64, uint, uint8, uint16, uint32, uint64:
			vars[idx] = utils.ToString(v)
		case float32:
			vars[idx] = strconv.FormatFloat(float64(v), 'f', -1, 32)
		case float64:
			vars[idx] = strconv.FormatFloat(v, 'f', -1, 64)
		case string:
			vars[idx] = escaper + strings.ReplaceAll(v, escaper, escaper+escaper) + escaper
		default:

//
// The `input_type`, `output_type` and `inference_type` can be float32 / qint8 /
// int8 / int16.
//
// Returns a partially quantized model if `fully_quantize` is false. Returns a
// non-OK status if the quantization fails.
//
// When `verify_numeric` is true, the model will have it's original float ops
// and NumericVerify ops to compare output values from the quantized and float
// ops.
//

    "'h_scale' type: 'float'} attr : { name: 'max_classes_per_detection' "
    "type: 'int'} attr : { name: 'max_detections' type: 'int'} attr : { "
    "name: 'nms_iou_threshold' type: 'float'} attr : { name: "
    "'nms_score_threshold' type: 'float'} attr : { name: 'num_classes' type: "
    "'int'} attr : { name: 'w_scale' type: 'float'} attr : { name: 'x_scale' "
    "type: 'float'} attr : { name: 'y_scale' type: 'float'} attr { name: "

}

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 {

	"unsafe"
)

type Struct struct {
	Int8       int8
	Int16      int16
	Int32      int32
	Int64      int64
	Uint8      uint8
	Uint16     uint16
	Uint32     uint32
	Uint64     uint64
	Float32    float32
	Float64    float64
	Complex64  complex64
	Complex128 complex128
	Array      [4]uint8
	Bool       bool
	BoolArray  [4]bool
}

type T struct {
	Int     int
	Uint    uint
	Uintptr uintptr