Port     uint16
	Priority uint16
	Weight   uint16
}

// byPriorityWeight sorts SRV records by ascending priority and weight.
type byPriorityWeight []*SRV

// shuffleByWeight shuffles SRV records by weight using the algorithm
// described in RFC 2782.
func (addrs byPriorityWeight) shuffleByWeight() {
	sum := 0
	for _, addr := range addrs {
		sum += int(addr.Weight)
	}
	for sum > 0 && len(addrs) > 1 {

metadata:
  name: valid-virtual-service
spec:
  hosts:
    - c
  http:
    - route:
      - destination:
          host: c
          subset: v1
        weight: 75
      - destination:
          host: c
          subset: v2

#include "tensorflow/core/protobuf/meta_graph.pb.h"

namespace mlir::quant::stablehlo {

// Performs int8 weight-only quantization on dot_general ops.
//
// The resulting `ModuleOp` contains quantized StableHLO ops serialized in
// `TF::XlaCallModuleOp`s. They are quantized using the weight constants, not
// relying on calibration.
class WeightOnlyPtqComponent : public Component {
 public:
  // Used for debugging purposes.

metadata:
  name: valid-virtual-service
spec:
  hosts:
    - c
  http:
    - route:
      - destination:
          host: c
          subset: v1
        weight: 75
      - destination:
          host: c
          subset: v2

func.func private @conv(%input: tensor<1x3x4x3xf32> {tf._user_specified_name = "input_tensor"}) -> tensor<*xf32> attributes {tf._construction_context = "kEagerRuntime", tf._input_shapes = [#tf_type.shape<1x3x4x3>]} {
  %weight = arith.constant dense_resource<__elided__> : tensor<2x3x3x2xf32>
  %bias = arith.constant dense<[7.11401462, 7.05456924]> : tensor<2xf32>

// RUN: stablehlo-quant-opt %s -split-input-file -verify-diagnostics \
// RUN:     -stablehlo-quantize-composite-functions | FileCheck --check-prefix=CHECK %s

// Test that per-tensor weight-only quantized dot_general op is produced when
// empty `weight_only_ptq` is provided.

module attributes {tf_saved_model.semantics} {
  func.func private @quantize_dot_general_per_tensor(%arg0: tensor<1x2xf32>) -> tensor<1x3xf32> attributes {tf._original_func_name = "main_0"} {

  static bool GetArithmeticCountForConvAndFullyconnectedOp(mlir::Operation* op,
                                                           int64_t* count) {
    auto weight = op->getOperand(1);
    auto weight_type =
        mlir::dyn_cast_or_null<mlir::RankedTensorType>(weight.getType());
    if (weight_type == nullptr || !weight_type.hasStaticShape()) return false;

    auto output = op->getResult(0);
    auto output_type =

// be 63 bytes or fewer.
func (w SRVWriter) AddSRV(priority, weight, port uint16, target string) error {
	targetName, err := dnsmessage.NewName(target)
	if err != nil {
		return err
	}
	w.a.wrote = true
	err = w.a.builder.SRVResource(w.header(), dnsmessage.SRVResource{
		Priority: priority,
		Weight:   weight,
		Port:     port,
		Target:   targetName,
	})
	if err != nil {

	if dst == nil {
		return nil, errors.New("ipvs destination should not be empty")
	}
	return &RealServer{
		Address:      dst.Address,
		Port:         dst.Port,
		Weight:       dst.Weight,
		ActiveConn:   dst.ActiveConnections,
		InactiveConn: dst.InactiveConnections,
	}, nil
}

// toIPVSService converts a VirtualServer to the equivalent IPVS Service structure.

// through a StableHLO <-> MHLO roundtrip to utilize the MHLOQuantToInt pass.
void AddStablehloQuantToIntPasses(OpPassManager& pm);

// Processes tensors with NCHW format (== (batch, channel, height, weight)) by
// converting them to NHWC formats along with extra optimizations such as
// constant folding the transpose->convolution pattern. This is useful when