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:

// It would likely be in a constraints package in the standard library.
type Numeric interface {
	~int|~int8|~int16|~int32|~int64|
		~uint|~uint8|~uint16|~uint32|~uint64|~uintptr|
		~float32|~float64|
		~complex64|~complex128
}

func DotProduct[T Numeric](s1, s2 []T) T {
	if len(s1) != len(s2) {
		panic("DotProduct: slices of unequal length")
	}
	var r T
	for i := range s1 {
		r += s1[i] * s2[i]

//
// 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: "

  with strategy.scope():
    # Create an mnist float model with the specified float state.
    model = FloatModel()
    optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)

  def train_step(features):
    inputs = tf.image.convert_image_dtype(
        features['image'], dtype=tf.float32, saturate=False)
    labels = tf.one_hot(features['label'], num_classes)

		{name: "MOVWD", argLength: 1, reg: fp11, asm: "MOVWD"},     // int32 -> float64
		{name: "TRUNCFW", argLength: 1, reg: fp11, asm: "TRUNCFW"}, // float32 -> int32
		{name: "TRUNCDW", argLength: 1, reg: fp11, asm: "TRUNCDW"}, // float64 -> int32
		{name: "MOVFD", argLength: 1, reg: fp11, asm: "MOVFD"},     // float32 -> float64

	case "uintptr":
		return fmt.Sprintf("%s(%#x)", t2, uintptr(v))
	case "float32":
		v := float32(v)
		if math.IsInf(float64(v), -1) {
			return "float32(math.Inf(-1))"
		}
		if math.IsInf(float64(v), +1) {
			return "float32(math.Inf(+1))"
		}
		return fmt.Sprintf("%s(%v)", t2, float64(v))
	case "float64":
		return fmt.Sprintf("%s(%v)", t2, float64(v))
	}
	panic(t2)

		{uint16(93), "(uint16) 93\n"},
		{uint32(93), "(uint32) 93\n"},
		{uint64(93), "(uint64) 93\n"},
		{uintptr(93), "(uintptr) 0x5d\n"},
		{ptrint(93), "(*int)(93)\n"},
		{float32(93.76), "(float32) 93.76\n"},
		{float64(93.76), "(float64) 93.76\n"},
		{complex64(93i), "(complex64) (0+93i)\n"},
		{complex128(93i), "(complex128) (0+93i)\n"},
		{bool(true), "(bool) true\n"},
		{bool(false), "(bool) false\n"},

}

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 {