// For each level, there should be two lines, one from direct log,
	// the other one from UDS server
	gotLogs := strings.Split(
		strings.TrimSuffix(string(out), "\n"), "\n")
	if want, got := len(cases)*2, len(gotLogs); want != got {
		t.Fatalf("Number of logs want %v, got %v logs: %v", want, got, gotLogs)
	}
	i := 0
	for _, l := range gotLogs {
		var parsedLog map[string]any
		assert.NoError(t, json.Unmarshal([]byte(l), &parsedLog))

//===----------------------------------------------------------------------===//

mlir::LogicalResult PwStreamResultsOp::verify() {
  if (getArgs().size() != getNames().size()) {
    return emitOpError()
           << "has a mismatch between the number of arguments and their names ("
           << getArgs().size() << " vs. " << getNames().size() << ")";
  }
  return mlir::success();
}

    const std::unordered_set<std::string>& aggregator_ops_to_ignore,
    SmallVector<Value>& statistics_outputs, SmallVector<StringRef>& ids,
    SmallVector<int32_t>& calibration_methods) {
  for (auto op : region.getOps<TF::CustomAggregatorOp>()) {
    if (aggregator_ops_to_ignore.count(op.getId().str())) continue;

    ids.push_back(op.getId());
    calibration_methods.push_back(op.getCalibrationMethod());

    }
  }
  return true;
}

GlobalTensorUsesMap CreateGlobalTensorUsesMap(ModuleOp module) {
  GlobalTensorUsesMap global_tensor_uses;

  SymbolTable symbol_table(module);
  for (auto func : module.getOps<func::FuncOp>()) {
    for (size_t i = 0, e = func.getNumArguments(); i < e; i++) {
      auto sym =
          func.getArgAttrOfType<SymbolRefAttr>(i, "tf_saved_model.bound_input");
      if (!sym) {
        continue;

    @Override
    ExecHandle build() {
        if (OperatingSystem.current().isWindows()) {
            def theArgs = ['/d', '/c', executable.replace('/', File.separator)] + getArgs()
            setArgs(theArgs) //split purposefully to avoid weird windows CI issue
            executable = 'cmd.exe'
        } else {
            executable = "${workingDir}/${executable}"
        }

  TF::LoadResourceDataflowAnalysis(solver);
  if (failed(solver.initializeAndRun(module))) return signalPassFailure();

  DenseSet<GlobalTensorOp> remaining_global_tensor_ops;
  {
    auto ops = module.getOps<GlobalTensorOp>();
    remaining_global_tensor_ops.insert(ops.begin(), ops.end());
  }

  for (auto global_tensor : remaining_global_tensor_ops) {
    // This pass assumes that all global tensors as immutable (e.g. by a

      /*is_qdq_conversion=*/false);

  quantization_driver.Initialize();
  ASSERT_TRUE(quantization_driver.PropagateParamsAndReturnIfChanged());
  EXPECT_THAT(quantization_driver.GetArgs(), Not(IsEmpty()));

  for (const auto& arg : quantization_driver.GetArgs()) {
    const QuantState& state = quantization_driver.GetArgQuantState(arg);
    EXPECT_TRUE(isa<quant::QuantizedType>(state.params));
  }

}

static LogicalResult convertTFGlobals(ModuleOp module) {
  OpBuilder globalBuilder(module.getBodyRegion());
  DenseMap<Operation *, std::string> opToName;
  for (auto globalTensor : module.getOps<tf_saved_model::GlobalTensorOp>()) {
    auto exportedNames = tf_saved_model::GetExportedNames(globalTensor);
    std::string name;
    if (exportedNames.empty()) {
      name = "global_ml_" + globalTensor.getSymName().str();

    return failure();
  }

  // Fetch all VarHandleOp.
  llvm::StringSet<> variable_names;
  llvm::SmallVector<TF::VarHandleOp, 4> var_ops;
  for (auto func_op : module.getOps<func::FuncOp>()) {
    for (auto var_handle_op : func_op.getOps<TF::VarHandleOp>()) {
      auto variable_name = GetVariableName(var_handle_op);
      if (variable_names.count(variable_name)) continue;
      var_ops.emplace_back(var_handle_op);

}

func (fmc *FakeMirrorClient) NumOfPods() int {
	fmc.mirrorPodLock.RLock()
	defer fmc.mirrorPodLock.RUnlock()
	return fmc.mirrorPods.Len()
}

func (fmc *FakeMirrorClient) GetPods() []string {
	fmc.mirrorPodLock.RLock()
	defer fmc.mirrorPodLock.RUnlock()
	return sets.List(fmc.mirrorPods)
}

func (fmc *FakeMirrorClient) GetCounts(podFullName string) (int, int) {
	fmc.mirrorPodLock.RLock()