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

	m.lock.Lock()
	defer m.lock.Unlock()
	return m.expMovingAvg
}

// ActiveWorkerStat is stat for active replication workers
type ActiveWorkerStat struct {
	Curr int     `json:"curr"`
	Avg  float32 `json:"avg"`
	Max  int     `json:"max"`
	hist metrics.Histogram
}

func newActiveWorkerStat(r metrics.Registry) *ActiveWorkerStat {
	h := metrics.NewHistogram(metrics.NewUniformSample(100))

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

pkg go/types, const Complex64 = 15
pkg go/types, const Complex64 BasicKind
pkg go/types, const FieldVal = 0
pkg go/types, const FieldVal SelectionKind
pkg go/types, const Float32 = 13
pkg go/types, const Float32 BasicKind
pkg go/types, const Float64 = 14
pkg go/types, const Float64 BasicKind
pkg go/types, const Int = 2
pkg go/types, const Int BasicKind
pkg go/types, const Int16 = 4

		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"`
	}

   ```
   @tf.py_function(Tout=tf.float32)
   def my_fun(x):
     print("This always executes eagerly.")
     return x+1
   ```

* `tf.lite`
    * Strided_Slice now supports `UINT32`.

* `tf.config.experimental.enable_tensor_float_32_execution`

func TestMigrateAutoIncrement(t *testing.T) {
	type AutoIncrementStruct struct {
		ID     int64   `gorm:"primarykey;autoIncrement"`
		Field1 uint32  `gorm:"column:field1"`
		Field2 float32 `gorm:"column:field2"`
	}

	if err := DB.AutoMigrate(&AutoIncrementStruct{}); err != nil {
		t.Fatalf("AutoMigrate err: %v", err)
	}

	const ROWS = 10
	for idx := 0; idx < ROWS; idx++ {

		default:
			p.errorf("bad int size for DATA argument: %d", sz)
		}
	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:

		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,