if intA > result {
				result = intA
			}
		}
		v.setInt(result)
		return nil
	}

	floatV, _ := v.ToFloat()
	floatA, _ := a.ToFloat()
	var result float64
	if !isMax {
		result = math.Min(floatV, floatA)
	} else {
		result = math.Max(floatV, floatA)
	}
	v.setFloat(result)
	return nil
}

func inferTypeAsTimestamp(v *Value) error {
	if s, ok := v.ToString(); ok {

				}
			case float64:
				field.ReflectValueOf(ctx, value).SetFloat(data)
			case float32:
				field.ReflectValueOf(ctx, value).SetFloat(float64(data))
			case int64:
				field.ReflectValueOf(ctx, value).SetFloat(float64(data))
			case int:
				field.ReflectValueOf(ctx, value).SetFloat(float64(data))
			case int8:
				field.ReflectValueOf(ctx, value).SetFloat(float64(data))
			case int16:

			Buckets: []float64{.05, .1, .25, .5, 1, 2.5, 5, 10},
		},
		[]string{"api"},
	)
	bucketHTTPRequestsDuration = prometheus.NewHistogramVec(
		prometheus.HistogramOpts{
			Name:    "s3_ttfb_seconds",
			Help:    "Time taken by requests served by current MinIO server instance per bucket",
			Buckets: []float64{.05, .1, .25, .5, 1, 2.5, 5, 10},
		},
		[]string{"api", "bucket"},
	)

		Peak:    peak,
		Curr:    curr,
		measure: rx.measure,
		N:       rx.N + o.N,
	}
}

func calcAvg(x, y float64, n1, n2 int64) float64 {
	if n1+n2 == 0 {
		return 0
	}
	avg := (x*float64(n1) + y*float64(n2)) / float64(n1+n2)
	return avg
}

// Add a new transfer
func (rx *XferStats) addSize(sz int64, t time.Duration) {
	if rx.measure == nil {

	}
	if count > 0 {
		lrg := XferStats{
			Avg:  lavg / float64(count),
			Curr: lcurr / float64(count),
			Peak: lpeak,
		}
		sml := XferStats{
			Avg:  savg / float64(count),
			Curr: scurr / float64(count),
			Peak: speak,
		}
		qs.XferStats[Large] = lrg
		qs.XferStats[Small] = sml
		qs.XferStats[Total] = XferStats{
			Avg:  (savg + lavg) / float64(count),
			Curr: (lcurr + scurr) / float64(count),

					// Since we are benchmarking n parallel servers we need to multiply by n.
					// This will give an estimate of the total ops/s.
					latency := float64(atomic.LoadInt64(&lat)) / float64(time.Millisecond)
					b.ReportMetric(float64(n)*float64(ops)/spent.Seconds(), "vops/s")
					b.ReportMetric(latency/float64(ops), "ms/op")
				}
			})
		}
	})
	b.Run("rpc", func(b *testing.B) {
		for par := 1; par <= 32; par *= 2 {

	Labels map[string]string

	// value captured in current cycle
	Current float64

	// Used when system provides cumulative (since uptime) values
	// helps in calculating the current value by comparing the new
	// cumulative value with previous one
	Cumulative float64

	Max   float64
	Avg   float64
	Sum   float64
	Count uint64
}

func init() {

type int64 int64

// float32 is the set of all IEEE 754 32-bit floating-point numbers.
type float32 float32

// float64 is the set of all IEEE 754 64-bit floating-point numbers.
type float64 float64

// complex64 is the set of all complex numbers with float32 real and
// imaginary parts.
type complex64 complex64

// complex128 is the set of all complex numbers with float64 real and
// imaginary parts.

		{Name: "FLOAT32", DBName: "ffloat32", BindNames: []string{"FLOAT32"}, DataType: schema.Float, Creatable: true, Updatable: true, Readable: true, Size: 32, Tag: `gorm:"column:ffloat32"`},
		{Name: "FLOAT64", DBName: "ffloat64", BindNames: []string{"FLOAT64"}, DataType: schema.Float, Creatable: true, Updatable: true, Readable: true, Size: 64, Tag: `gorm:"column:ffloat64"`},

		if i%2 == 0 {
			l.Add(trace[i], trace[i])
		} else {
			if _, ok := l.Get(trace[i]); ok {
				hit++
			} else {
				miss++
			}
		}
	}
	b.Logf("hit: %d miss: %d ratio: %f", hit, miss, float64(hit)/float64(hit+miss))
}

func BenchmarkLRU_Freq_NoExpire(b *testing.B) {
	l := lru.NewLRU[int64, int64](8192, nil, 0)

	trace := make([]int64, b.N*2)
	for i := 0; i < b.N*2; i++ {
		if i%2 == 0 {