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());

      component.Run(*module_op, quantization_config);

  EXPECT_THAT(pre_calibration_result, IsOk());

  SmallVector<func::FuncOp> func_ops;
  for (auto func_op : pre_calibration_result->getOps<func::FuncOp>()) {
    func_ops.push_back(func_op);
  }
  ASSERT_THAT(func_ops, SizeIs(2));
  EXPECT_THAT(func_ops, Contains(HasSymName("main")));

        absl::StrCat("failed to fetch device manager: ", status.message()));

  // Fetch all varHandleOp in the function.
  llvm::SmallVector<TF::VarHandleOp, 4> var_ops;
  for (auto var_handle_op : block.getOps<TF::VarHandleOp>())
    var_ops.emplace_back(var_handle_op);

  // Get resources from Session.
  auto resource_tensors_or = GetResourcesFromSession(var_ops, session);
  if (!resource_tensors_or.ok())

    }
  }
  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;

           call_op.getOperands())) {
    arg_mapper.map(func_arg, operand);
  }

  Region& function_body = func_op.getBody();
  IRMapping new_op_mapper;
  for (Operation& op : function_body.getOps()) {
    if (llvm::isa<func::ReturnOp>(op)) {
      for (auto [call_result, return_value] :
           llvm::zip_equal(call_op.getResults(), op.getOperands())) {
        Value new_result = new_op_mapper.lookup(return_value);

  // if no trim_funcs_allowlist_ is specified, this pass is a no-op.
  if (trim_funcs_allowlist_.empty()) return false;

  llvm::SmallVector<func::FuncOp, 4> funcs_to_trim;
  for (auto func : getOperation().getOps<func::FuncOp>()) {
    if (llvm::is_contained(trim_funcs_allowlist_, func.getName())) {
      // If no main is specified in the allowlist, use the 1st func
      // in trim_funcs_allowlist as the main.

  temp_file.os().flush();

  // Replace filename constant ops to use the temporary file.
  MLIRContext* context = &getContext();

  for (func::FuncOp func : module.getOps<func::FuncOp>()) {
    llvm::SmallVector<arith::ConstantOp, 4> constant_ops(
        func.getOps<arith::ConstantOp>());
    for (auto op : constant_ops) {
      ShapedType shaped_type =
          RankedTensorType::get({1}, StringType::get(context));

  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

}

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();

  }
  void runOnOperation() override {
    auto module = getOperation();
    TensorArraySideEffectAnalysis tensor_array_side_effect_analysis(module);

    for (auto func_op : module.getOps<mlir::func::FuncOp>()) {
      func_op.emitRemark() << "HasAtMostTensorArrayEffect: "
                           << tensor_array_side_effect_analysis
                                  .HasAtMostTensorArrayEffect(func_op);
    }