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

		}
	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, int(sz), valueAddr.Val.(string))

	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"`
	}

		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,

    return Float.compare(a, b);
  }

  /**
   * Returns {@code true} if {@code value} represents a real number. This is equivalent to, but not
   * necessarily implemented as, {@code !(Float.isInfinite(value) || Float.isNaN(value))}.
   *
   * <p>Prefer {@link Float#isFinite(float)} instead.
   *
   * @since 10.0
   */
  @InlineMe(replacement = "Float.isFinite(value)")
  public static boolean isFinite(float value) {

pkg math, func Log10(float64) float64
pkg math, func Log1p(float64) float64
pkg math, func Log2(float64) float64
pkg math, func Logb(float64) float64
pkg math, func Max(float64, float64) float64
pkg math, func Min(float64, float64) float64
pkg math, func Mod(float64, float64) float64
pkg math, func Modf(float64) (float64, float64)
pkg math, func NaN() float64
pkg math, func Nextafter(float64, float64) float64