using ::tensorflow::kDefaultServingSignatureDefKey;
using ::tensorflow::kImportModelDefaultGraphFuncName;

// Returns true iff the function's symbol is public.
bool IsPublicFuncOp(func::FuncOp func_op) {
  return SymbolTable::getSymbolVisibility(&*func_op) ==
         SymbolTable::Visibility::Public;
}

}  // namespace

func::FuncOp FindMainFuncOp(ModuleOp module_op) {
  if (const auto main_func_op = module_op.lookupSymbol<func::FuncOp>(

  OpPassManager &func_pm = pm.nest<func::FuncOp>();

  // First operates on the executor dialect:
  // - remove dead islands.
  // - fuse islands as much as possible.
  // - materialize the eventual "pass-through" ops by inlining their content.
  func_pm.addPass(tf_executor::CreateTFExecutorGraphPruningPass());
  func_pm.addPass(tf_executor::CreateTFExecutorIslandCoarseningPass());

 public:
  void runOnOperation() override;
};

void InferenceMetricsPass::runOnOperation() {
  bool has_tpu_partitioned_call = false;
  ModuleOp module = getOperation();

  for (auto func_op : module.getOps<func::FuncOp>()) {
    func_op->walk(
        [&](TF::TPUPartitionedCallOp op) { has_tpu_partitioned_call = true; });

    if (has_tpu_partitioned_call) break;
  }

  std::string has_tpu_partitioned_call_str =

    return_operands.push_back(cloned_output);
  }
  function_builder.create<mlir::func::ReturnOp>(new_func.getLoc(),
                                                return_operands);
  ops_added.func_op = new_func;
  module.push_back(new_func);
  return new_func;
}

void ExtractSubgraphToFunc(const Subgraph& subgraph, OpBuilder& builder,
                           ModuleOp& module, OpsAdded& ops_added) {

    : tfr_module_(tfr_module), pm_(tfr_module_.getContext()) {
  mlir::OpPassManager& func_pm = pm_.nest<mlir::func::FuncOp>();

  // Prepare the imported graph.
  func_pm.addPass(mlir::CreateExecutorDialectToFunctionalConversionPass());

  // Run TFR lowering, inlining and raising to tf.
  func_pm.addPass(mlir::TFR::CreateDecomposeTFOpsPass(tfr_module_));
  func_pm.addPass(mlir::TFR::CreateRaiseToTFOpsPass(

  // Insert CalibrationStatisticsSaverOp to the end of each region.
  for (auto func_op : module_op.getOps<func::FuncOp>()) {
    int32_t output_file_idx = 0;
    StringRef func_name = func_op.getSymName();

    func_op.walk([&output_file_idx, &ctx, &func_name, &aggregator_ops_to_ignore,
                  this](Operation* op) {
      for (Region& region : op->getRegions()) {

  return op->getName().getStringRef().str() == kClusterFuncOpName;
}

void VerifyInputDialectToExecutorPass::runOnOperation() {
  Operation* func_op = getOperation();

  auto walk_result = func_op->walk([&](Operation* op) {
    if (!tensorflow::tf2xla::internal::IsInBridgeAcceptableDialects(op)) {
      std::string error = "op is in dialect " +

    : public impl::VerifyClusteringPassBase<VerifyClusteringPass> {
 public:
  void runOnOperation() override;
};

void VerifyClusteringPass::runOnOperation() {
  Operation* func_op = getOperation();

  auto walk_result = func_op->walk([&](Operation* op) {
    if (!tensorflow::tf2xla::internal::IsInBridgeAcceptableDialects(op)) {
      std::string error = "op is in dialect " +

 public:
  void runOnOperation() override;
};

void InputMetricsLoweringPass::runOnOperation() {
  bool has_dynamic_op = false;
  Operation* func_op = getOperation();

  func_op->walk([&](Operation* op) {
    auto abstractOp = op->getRegisteredInfo();
    if (!abstractOp) return WalkResult::advance();

    if (mlir::mhlo::IsDynamicPadderOp(abstractOp->getTypeID())) {

  module_op->walk([&](func::FuncOp func_op) {
    FunctionName func_name = func_op.getSymName().str();
    existing_func_names.insert(func_name);
    // We may retrieve the original function's name from the attribute.
    // Functions without this attribute are ignored.
    auto original_func_name =
        func_op->getAttrOfType<StringAttr>("tf._original_func_name");
    if (original_func_name) {