}
			return err
		}
	case reflect.Float32, reflect.Float64:
		field.Set = func(ctx context.Context, value reflect.Value, v interface{}) (err error) {
			switch data := v.(type) {
			case **float64:
				if data != nil && *data != nil {
					field.ReflectValueOf(ctx, value).SetFloat(**data)
				}
			case **float32:
				if data != nil && *data != nil {

// Range: -9223372036854775808 through 9223372036854775807.
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

2.718281828459045   float32     2.718281828459045 rounds to 2.7182817 which is in the set of float32 values
-1e-1000            float64     -1e-1000 rounds to IEEE -0.0 which is further simplified to 0.0
0i                  int         0 is an integer value
(42 + 0i)           float32     42.0 (with zero imaginary part) is in the set of float32 values
</pre>

<pre>

   ```
   @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`

		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:

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++ {

  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,