{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

			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`},

		{`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`},

  %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>

        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

            """
        ),
    ] = None,
    gt: Annotated[
        Optional[float],
        Doc(
            """
            Greater than. If set, value must be greater than this. Only applicable to
            numbers.
            """
        ),
    ] = None,
    ge: Annotated[
        Optional[float],
        Doc(
            """

	{"HEX", "{{print 0X123ABC}}", "1194684", tVal, true},
	{"float", "{{print 123.4}}", "123.4", tVal, true},
	{"float _", "{{print 0_0_1_2_3.4}}", "123.4", tVal, true},
	{"hex float", "{{print +0x1.ep+2}}", "7.5", tVal, true},
	{"hex float _", "{{print +0x_1.e_0p+0_2}}", "7.5", tVal, true},
	{"HEX float", "{{print +0X1.EP+2}}", "7.5", tVal, true},

    const QuantizationParametersT& quantized_quant_params, const int bit_num,
    const bool symmetric) {
  const float eps = 1e-7;
  ASSERT_THAT(*float_quant_params.min(), SizeIs(1));
  ASSERT_THAT(*float_quant_params.max(), SizeIs(1));
  float float_min = std::min(0.f, float_quant_params.min()->Get(0));
  float float_max = std::max(0.f, float_quant_params.max()->Get(0));
  if (symmetric) {

        UniformQuantizeFunctionCallPattern::Match(
            input_uniform_quantize_call_op);

    const float input_inverse_scale_value =
        input_uniform_quantize_call_pattern->GetInverseScalesValueAttr()
            .getSplatValue<APFloat>()
            .convertToFloat();
    const float input_scale_value = 1.0 / input_inverse_scale_value;

    const int8_t input_zero_point_value =