const int64_t int_value = ap_int_value.getSExtValue();

      const float real = (int_value - zp) * scale;

      auto real_int = absl::bit_cast<int32_t>(real);
      return APInt(/*numBits=*/32, real_int);
    };

    auto dequant_values =
        mlir::cast<DenseIntOrFPElementsAttr>(input_values)
            .mapValues(FloatType::getF32(rewriter.getContext()),

quant::QuantParams DefaultQuantParamsPass::GetDefaultQuantParams(
    Builder builder) {
  if (!default_quant_params_) {
    default_quant_params_ = quantfork::fakeQuantAttrsToType(
        builder.getUnknownLoc(),
        /*numBits=*/8, default_min_, default_max_, /*narrowRange=*/false,
        builder.getF32Type(), is_signed_);
  }
  return default_quant_params_;
}

// Creates an instance of the default quant parameters pass.

def LegalizeFakeQuantToDequantizeQuantize : Pat<
  (TF_FakeQuantWithMinMaxArgsOp $inputs, $min, $max, $num_bits, $narrow_range),
  (TFL_DequantizeOp
   (TFL_QuantizeOp $inputs,
                   (ConvertToQuantTypeFromAttrs $inputs, $min, $max,
                    $num_bits, $narrow_range)))>;

def LegalizeFill : Pat<(TF_FillOp $arg, $value), (TFL_FillOp $arg, $value)>;

  auto bias_value_type = RankedTensorType::getChecked(
      loc, std::move(bias_shape), rewriter.getI32Type());
  auto bias_value = DenseIntElementsAttr::get(
      bias_value_type, APInt(/*numBits=*/32, /*value=*/0, /*isSigned=*/true));

  return rewriter.create<TFL::QConstOp>(
      loc, /*output=*/TypeAttr::get(bias_type), /*value=*/bias_value);
}

  %0 = "tf.FakeQuantWithMinMaxVars"(%arg0, %cst, %cst_0) {
    device = "", narrow_range = false, num_bits = 8 : i64
  } : (tensor<3xf32>, tensor<f32>, tensor<f32>) -> tensor<3xf32>
  %1 = "tf.FakeQuantWithMinMaxVarsPerChannel"(%arg1, %cst_1, %cst_2) {
    device = "", narrow_range = true, num_bits = 8 : i64
  } : (tensor<4x3xf32>, tensor<3xf32>, tensor<3xf32>) -> tensor<4x3xf32>

	var values []string
	values = append(values, s.Label[k]...)
	numLabels := s.NumLabel[k]
	numUnits := s.NumUnit[k]
	if len(numLabels) != len(numUnits) && len(numUnits) != 0 {
		return values
	}
	for i, numLabel := range numLabels {
		var value string
		if len(numUnits) != 0 {
			value = measurement.ScaledLabel(numLabel, numUnits[i], outputUnit)
		} else {
			value = measurement.ScaledLabel(numLabel, "", "")
		}

  ^bb0(%arg1: tensor<8xf32>, %arg2: tensor<f32>, %arg3: tensor<f32>):
    %2 = "tf.FakeQuantWithMinMaxVars"(%arg1, %arg2, %arg3) {num_bits = 5, narrow_range = false} : (tensor<8xf32>, tensor<f32>, tensor<f32>) -> tensor<8xf32>
    "tfl.yield"(%2) : (tensor<8xf32>) -> ()
  }) {num_bits = 5, narrow_range = false} :  (tensor<8xf32>, tensor<f32>, tensor<f32>) -> tensor<8xf32>
  func.return %rst : tensor<8xf32>

  ^bb0(%arg1: tensor<8xf32>, %arg2: tensor<f32>, %arg3: tensor<f32>):
    %2 = "tf.FakeQuantWithMinMaxVars"(%arg1, %arg2, %arg3) {num_bits = 3, narrow_range = false} : (tensor<8xf32>, tensor<f32>, tensor<f32>) -> tensor<8xf32>
    "tfl.yield"(%2) : (tensor<8xf32>) -> ()
  }) {num_bits = 3, narrow_range = false} :  (tensor<8xf32>, tensor<f32>, tensor<f32>) -> tensor<8xf32>
  func.return %rst : tensor<8xf32>

  %1 = "quantfork.qcast"(%0) : (tensor<8xf32>) -> tensor<8x!quant.uniform<i8:f32, 1.000000e+00:-128>>
  func.return %1 : tensor<8x!quant.uniform<i8:f32, 1.000000e+00:-128>>

// CHECK:  %0 = "tf.FakeQuantWithMinMaxVars"(%arg0, %cst, %cst_0) <{narrow_range = false, num_bits = 5 : i64}>

// CHECK-NEXT:     } ]
// CHECK-NEXT:     signature_defs: [ ]
// CHECK-NEXT:   }

// IMPORT: "tfl.fake_quant"(%arg0) <{max = 1.400000e+00 : f32, min = 3.000000e-01 : f32, narrow_range = false, num_bits = 6 : i32}>

  %0 = "tfl.fake_quant"(%arg0) {num_bits = 6 : i32, narrow_range = false, min = 0.3:f32, max = 1.4:f32} : (tensor<4 x f32>) -> tensor<4 x f32>
  func.return %0 : tensor<4xf32>