{name: "FMOVSconst", argLength: 0, reg: fp01, aux: "Float64", asm: "FMOVS", typ: "Float32", rematerializeable: true}, // auxint as 64-bit float, convert to 32-bit float
		{name: "FMOVDconst", argLength: 0, reg: fp01, aux: "Float64", asm: "FMOVD", typ: "Float64", rematerializeable: true}, // auxint as 64-bit float

		opname        rune
		op            func(z, x, y *Float) *Float
	}{
		{0, 0, 0, 0, '+', (*Float).Add},
		{0, 1, 2, 3, '+', (*Float).Add},
		{1, 2, 0, 2, '+', (*Float).Add},
		{2, 0, 1, 1, '+', (*Float).Add},

		{0, 0, 0, 0, '-', (*Float).Sub},
		{0, 1, 2, -1, '-', (*Float).Sub},
		{1, 2, 0, 2, '-', (*Float).Sub},
		{2, 0, 1, -1, '-', (*Float).Sub},

		{0, 0, 0, 0, '*', (*Float).Mul},
		{0, 1, 2, 2, '*', (*Float).Mul},

	{float32(Copysign(0, -1)), -1},
	{float32(NaN()), 0},
	{float32(NaN()), float32(NaN())},
}
var nextafter32SC = []float32{
	0,
	0,
	-1.401298464e-45, // Float32frombits(0x80000001)
	float32(NaN()),
	1.401298464e-45, // Float32frombits(0x00000001)
	float32(Copysign(0, -1)),
	float32(Copysign(0, -1)),
	-1.401298464e-45, // Float32frombits(0x80000001)
	float32(NaN()),
	float32(NaN()),
}

			return []string{"-mabi=64", "-mhard-float"}
		} else if gomips64 == "softfloat" {
			return []string{"-mabi=64", "-msoft-float"}
		}
	case "mips", "mipsle":
		if gomips == "hardfloat" {
			return []string{"-mabi=32", "-mfp32", "-mhard-float", "-mno-odd-spreg"}
		} else if gomips == "softfloat" {
			return []string{"-mabi=32", "-msoft-float"}
		}
	case "loong64":

		{`package e0; const _ = float32( 1e-200)`, `float32(1e-200)`, `float32`, `0`},
		{`package e1; const _ = float32(-1e-200)`, `float32(-1e-200)`, `float32`, `0`},
		{`package e2; const _ = float64( 1e-2000)`, `float64(1e-2000)`, `float64`, `0`},
		{`package e3; const _ = float64(-1e-2000)`, `float64(-1e-2000)`, `float64`, `0`},

      )

    return inputs, outputs

  def _create_data_generator(
      self,
      input_key: str,
      shape: Sequence[int],
      minval: float = -1.0,
      maxval: float = 1.0,
      dtype: dtypes.DType = dtypes.float32,
      num_examples: int = 8,
  ) -> repr_dataset.RepresentativeDataset:
    """Creates a data generator to be used as representative dataset.

	{"%#v", []int32(nil), "[]int32(nil)"},
	{"%#v", 1.2345678, "1.2345678"},
	{"%#v", float32(1.2345678), "1.2345678"},

	// Whole number floats are printed without decimals. See Issue 27634.
	{"%#v", 1.0, "1"},
	{"%#v", 1000000.0, "1e+06"},
	{"%#v", float32(1.0), "1"},
	{"%#v", float32(1000000.0), "1e+06"},

	// Only print []byte and []uint8 as type []byte if they appear at the top level.

        rng.uniform(low=0.0, high=1.0, size=static_input_shape).astype(
            np.float32
        )
    )

    def data_gen() -> repr_dataset.RepresentativeDataset:
      for _ in range(100):
        yield {
            'input_tensor': rng.uniform(
                low=0.0, high=1.0, size=static_input_shape
            ).astype(np.float32)
        }

    dataset_path = self.create_tempfile('tfrecord').full_path

	Int   int     `xml:",attr"`
	Named int     `xml:"int,attr"`
	Float float64 `xml:",attr"`
	Uint8 uint8   `xml:",attr"`
	Bool  bool    `xml:",attr"`
	Str   string  `xml:",attr"`
	Bytes []byte  `xml:",attr"`
}

type AttrsTest struct {
	Attrs []Attr  `xml:",any,attr"`
	Int   int     `xml:",attr"`
	Named int     `xml:"int,attr"`
	Float float64 `xml:",attr"`
	Uint8 uint8   `xml:",attr"`
	Bool  bool    `xml:",attr"`

  %2 = "tfl.add"(%arg0, %arg3) {tac.device = "GPU", fused_activation_function = "RELU6", tac.inference_type = "FLOAT"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
  %3 = "tfl.pack"(%1, %2) {tac.device = "CPU", tac.inference_type = "FLOAT", axis = 0 : i32, values_count = 2 : i32} : (tensor<1xf32>, tensor<1xf32>) -> tensor<2x1xf32>