double scale_product = in_spec.getScale() * w_spec.getScale();
  if (fabs(scale_product - b_spec.getScale()) >= 1e-6) return failure();

  // input multipliers
  input_multipliers->append(3, kUnitQuantizedMultiplier);

  // output multipliers
  double real_multiplier = scale_product / o_spec.getScale();
  output_multipliers->push_back(QuantizeMultiplier(real_multiplier));

    // Invoke BatchNormInferenceOp
    ::mlir::FloatAttr epsilon = bn_op.getEpsilonAttr();
    ::mlir::Value batch_norm = rewriter.create<mhlo::BatchNormInferenceOp>(
        bn_op.getLoc(), inputs, bn_op.getScale(), bn_op.getOffset(), mean,
        variance, epsilon, rewriter.getI64IntegerAttr(feature_index));

    // Return normalized values, mean, variable.

//===----------------------------------------------------------------------===//
// Selu op patterns.
//===----------------------------------------------------------------------===//

def getScale : NativeCodeCall<
  "GetScalarOfType(getElementTypeOrSelf($0), 1.0507009873554804934193349852946)"
  >;

def getScaledAlpha : NativeCodeCall<

  if (auto uniform_type = llvm::dyn_cast<UniformQuantizedType>(element_type)) {
    if (multiplier_attr.isSplat()) {
      double new_scale = multiplier_array.front() * uniform_type.getScale();
      new_qtype = UniformQuantizedType::get(
          uniform_type.getFlags(), uniform_type.getStorageType(),
          uniform_type.getExpressedType(), new_scale,

	return nil, errors.New("ApplyStatus() is not implemented for conversionClient")
}

func (c conversionClient) GetScale(ctx context.Context, name string, options metav1.GetOptions) (result *autoscalingv1.Scale, err error) {
	// This is not used by RSC.
	return nil, errors.New("GetScale() is not implemented for conversionClient")
}

    return qmax - std::round((storage_type_max - zero_point) / rate);
  };
  if (auto q_type = dyn_cast<UniformQuantizedType>(type)) {
    const double scale = recalculate_scale(q_type.getScale());
    const double zero_point = recalculate_zero_point(q_type.getZeroPoint());
    return UniformQuantizedType::get(q_type.getFlags(), q_type.getStorageType(),

      return failure();
    }
    auto scale_cast = rewriter.create<CastOp>(
        loc, qparams_op.getScale().getType(), scale_op.getOutput());
    auto zp_cast = rewriter.create<CastOp>(loc, qparams_op.getZp().getType(),
                                           zp_op.getOutput());

    qparams_op.getScale().replaceAllUsesWith(scale_cast.getOut());
    qparams_op.getZp().replaceAllUsesWith(zp_cast.getOut());

                                     .cast<UniformQuantizedType>();
    new_filter_quantized_type = CreateI8F32UniformQuantizedType(
        filter_constant_op->getLoc(), *rewriter.getContext(),
        filter_quantized_type.getScale(), filter_quantized_type.getZeroPoint(),
        /*narrow_range=*/true);
  }

  // Required because the quantized dimension is changed from 3 -> 0.
  auto new_filter_result_type = RankedTensorType::getChecked(

  const UniformQuantizedType quantized_type =
      CreateI8F32UniformQuantizedType(UnknownLoc::get(&ctx_), ctx_,
                                      /*scale=*/8.0, /*zero_point=*/99);

  EXPECT_EQ(quantized_type.getScale(), 8.0);
  EXPECT_EQ(quantized_type.getZeroPoint(), 99);
}

class CreateI32F32UniformQuantizedTypeTest : public Test {
 protected:
  CreateI32F32UniformQuantizedTypeTest() : ctx_() {

    auto q_type = mlir::dyn_cast_or_null<quant::UniformQuantizedType>(
        input_type.getElementType());
    if (!q_type) return failure();

    const float scale = q_type.getScale();
    const float zp = q_type.getZeroPoint();

    auto input_values = input_dequant.getValue();

    // mapValues always takes a function returning APInt, even when the output
    // is actually float.