"enable post training quantization. Only used in tests">,
      Option<"legacy_float_scale_", "legacy-float-scale", "bool", "false",
             "calculate quantization scales in float instead of double">,
      Option<"disable_per_channel_", "disable-per-channel", "bool", "false",
             "Whether disable per-channel quantized weights.">,

	case abi.Uint64:
		print(typestring, "(", *(*uint64)(eface.data), ")")
	case abi.Uintptr:
		print(typestring, "(", *(*uintptr)(eface.data), ")")
	case abi.Float32:
		print(typestring, "(", *(*float32)(eface.data), ")")
	case abi.Float64:
		print(typestring, "(", *(*float64)(eface.data), ")")
	case abi.Complex64:
		print(typestring, *(*complex64)(eface.data))
	case abi.Complex128:
		print(typestring, *(*complex128)(eface.data))

		ve float64 = 3.9496598225815571993e-3
	)

	testKe = make([]uint32, 256)
	testWe = make([]float32, 256)
	testFe = make([]float32, 256)

	q := ve / math.Exp(-de)
	testKe[0] = uint32((de / q) * m2)
	testKe[1] = 0
	testWe[0] = float32(q / m2)
	testWe[255] = float32(de / m2)
	testFe[0] = 1.0
	testFe[255] = float32(math.Exp(-de))
	for i := 254; i >= 1; i-- {
		de = -math.Log(ve/de + math.Exp(-de))

	case reflect.Uint64:
		e.uint64(v.Uint())

	case reflect.Float32:
		e.uint32(math.Float32bits(float32(v.Float())))
	case reflect.Float64:
		e.uint64(math.Float64bits(v.Float()))

	case reflect.Complex64:
		x := v.Complex()
		e.uint32(math.Float32bits(float32(real(x))))
		e.uint32(math.Float32bits(float32(imag(x))))
	case reflect.Complex128:
		x := v.Complex()

}

func (v Value) float(method string) float64 {
	if !v.isNumber() {
		panic(&ValueError{method, v.Type()})
	}
	if v.ref == valueZero.ref {
		return 0
	}
	return *(*float64)(unsafe.Pointer(&v.ref))
}

// Float returns the value v as a float64.
// It panics if v is not a JavaScript number.
func (v Value) Float() float64 {
	return v.float("Value.Float")
}

table QuantizationParameters {
  // These four parameters are the asymmetric linear quantization parameters.
  // Given a quantized value q, the corresponding float value f should be:
  //   f = scale * (q - zero_point)
  // For other quantization types, the QuantizationDetails below is used.
  min:[float];  // For importing back into tensorflow.
  max:[float];  // For importing back into tensorflow.

	slices.Sort(pauses)
	var total uint64
	for _, ns := range pauses {
		total += ns
	}
	// ns/op for this benchmark is average STW time.
	b.ReportMetric(float64(total)/float64(b.N), "ns/op")
	b.ReportMetric(float64(pauses[len(pauses)*95/100]), "p95-ns/STW")
	b.ReportMetric(float64(pauses[len(pauses)*50/100]), "p50-ns/STW")
}

func BenchmarkPoolExpensiveNew(b *testing.B) {
	// Populate a pool with items that are expensive to construct

		t.Error(err)
	}
}

func TestTypeToPtrPtrPtrPtrType(t *testing.T) {
	type Type2 struct {
		A ****float64
	}
	t2 := Type2{}
	t2.A = new(***float64)
	*t2.A = new(**float64)
	**t2.A = new(*float64)
	***t2.A = new(float64)
	****t2.A = 27.4
	t2pppp := new(***Type2)
	if err := encAndDec(t2, t2pppp); err != nil {
		t.Fatal(err)
	}

        rng.uniform(low=0.0, high=1.0, size=static_input_shape).astype(
            np.float32
        )
    )

    def data_gen() -> repr_dataset.RepresentativeDataset:
      for _ in range(100):
        yield {
            'input_tensor': rng.uniform(
                low=0.0, high=1.0, size=static_input_shape
            ).astype(np.float32)
        }

    dataset_path = self.create_tempfile('tfrecord').full_path

}

// Float64 returns, as a float64, a pseudo-random number in the half-open interval [0.0,1.0)
// from the default Source.
func Float64() float64 { return globalRand.Float64() }

// Float32 returns, as a float32, a pseudo-random number in the half-open interval [0.0,1.0)
// from the default Source.
func Float32() float32 { return globalRand.Float32() }