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 {

			scanIntoMap(mapValue, values, columns)
			*dest = append(*dest, mapValue)
		}
	case *int, *int8, *int16, *int32, *int64,
		*uint, *uint8, *uint16, *uint32, *uint64, *uintptr,
		*float32, *float64,
		*bool, *string, *time.Time,
		*sql.NullInt32, *sql.NullInt64, *sql.NullFloat64,
		*sql.NullBool, *sql.NullString, *sql.NullTime:
		for initialized || rows.Next() {
			initialized = false
			db.RowsAffected++

			NumericRegexp: nil,
			Vars:          []interface{}{"jinzhu", 1, float32(999.99), true, []byte("12345"), tt, &tt, nil, "w@g.\"com", myrole, pwd, &js, &es},

pkg math/big, method (*Float) SetInf(bool) *Float
pkg math/big, method (*Float) SetInt(*Int) *Float
pkg math/big, method (*Float) SetInt64(int64) *Float
pkg math/big, method (*Float) SetMantExp(*Float, int) *Float
pkg math/big, method (*Float) SetMode(RoundingMode) *Float
pkg math/big, method (*Float) SetPrec(uint) *Float
pkg math/big, method (*Float) SetRat(*Rat) *Float

		Int64Field         int64
		UIntField          uint
		UInt8Field         uint8
		UInt16Field        uint16
		UInt32Field        uint32
		UInt64Field        uint64
		Float32Field       float32
		Float64Field       float64
		StringField        string
		TimeField          time.Time
		TimePtrField       *time.Time
		EmbeddedStruct     `gorm:"embedded"`
		*EmbeddedPtrStruct `gorm:"embedded"`
	}

    *   Layers now default to `float32`, and automatically cast their inputs to
        the layer's dtype. If you had a model that used `float64`, it will
        probably silently use `float32` in TensorFlow2, and a warning will be
        issued that starts with Layer "layer-name" is casting an input tensor
        from dtype float64 to the layer's dtype of float32. To fix, either set
        the default dtype to float64 with

	type MigrateNullableColumn struct {
		ID    uint
		Bonus float64 `gorm:"not null"`
		Stock float64
	}

	DB.Migrator().DropTable(&MigrateNullableColumn{})

	DB.AutoMigrate(&MigrateNullableColumn{})

	type MigrateNullableColumn2 struct {
		ID    uint
		Bonus float64
		Stock float64 `gorm:"not null"`
	}

		}
	case obj.TYPE_FCONST:
		switch sz {
		case 4:
			nameAddr.Sym.WriteFloat32(p.ctxt, nameAddr.Offset, float32(valueAddr.Val.(float64)))
		case 8:
			nameAddr.Sym.WriteFloat64(p.ctxt, nameAddr.Offset, valueAddr.Val.(float64))
		default:
			p.errorf("bad float size for DATA argument: %d", sz)
		}
	case obj.TYPE_SCONST:
		nameAddr.Sym.WriteString(p.ctxt, nameAddr.Offset, sz, valueAddr.Val.(string))

		return globalVeeamForceSC
	}
	return s
}

type ordered interface {
	~int | ~int8 | ~int16 | ~int32 | ~int64 | ~uint | ~uint8 | ~uint16 | ~uint32 | ~uint64 | ~uintptr | ~float32 | ~float64 | string
}

// mapKeysSorted returns the map keys as a sorted slice.
func mapKeysSorted[Map ~map[K]V, K ordered, V any](m Map) []K {
	res := make([]K, 0, len(m))
	for k := range m {

  ASSERT_EQ(TF_OK, TF_GetCode(s.get())) << TF_Message(s.get());
  ASSERT_NE(*func, nullptr);
}

REGISTER_OP("CustomOp")
    .Output("output: float32")
    .Attr("index: int")
    .SetShapeFn(tensorflow::shape_inference::UnknownShape);

void NodeWithPlaceholderAttrHelper(TF_Graph* graph, TF_Status* s,