Output arg1 = ops::_Arg(s.WithOpName("arg1"), DT_RESOURCE, 1);
    Output arg2 = ops::_Arg(s.WithOpName("arg2"), DT_INT32, 2);
    auto ret0 = ops::_Retval(s.WithOpName("ret0"), arg1, 0);
    auto ret1 = ops::_Retval(s.WithOpName("ret1"), arg0, 1);
    auto ret2 = ops::_Retval(s.WithOpName("ret2"), arg2, 2);
    std::unique_ptr<Graph> g(new Graph(OpRegistry::Global()));
    TF_CHECK_OK(s.ToGraph(g.get()));

  {
    tensorflow::Scope s = tensorflow::Scope::NewRootScope();
    Output arg = ops::_Arg(s.WithOpName("arg"), DT_INT32, 0);
    Output identity = ops::Identity(s.WithOpName("identity_true_fn"), arg);
    ops::_Retval retval(s.WithOpName("retval"), identity, 0);
    std::unique_ptr<Graph> g(new Graph(OpRegistry::Global()));
    TF_CHECK_OK(s.ToGraph(g.get()));
    auto node_name_image = g->BuildNodeNameIndex();

  might become the name of Retval nodes as well (with an index suffix if there
  are multiple output tensors from one node). Since Retval nodes are not used in
  SavedModel, this function removes them and restore the names to the actual
  output tensors.

  Args:
    graph_def: the converted GraphDef.

  Returns:
    The GraphDef with Retval nodes removed and output tensor names restored.

  add_attrs(g.node());

  auto out0 = ops::_Retval(scope.WithOpName("b_identity_0_retval_RetVal"),
                           b_identity, 0);
  auto out1 = ops::_Retval(scope.WithOpName("e_0_retval_RetVal"), e, 1);
  auto out2 = ops::_Retval(scope.WithOpName("g_0_retval_RetVal"), g, 2);
  auto out3 =
      ops::_Retval(scope.WithOpName("readu_0_retval_RetVal"), read_u, 3);

  // Computes the permutation to produce the correct retval order, and update
  // the argument indices.
  for (int i = 0; i < num_retvals; ++i) {
    int index;
    TF_RETURN_IF_ERROR(GetIndexAttr(*retvals[i], num_retvals, &index));
    (*output_permutation)[index] = i;
    retvals[i]->AddAttr("index", i);
  }

  AddNodeAttr(kXlaClusterIdAttr, call_def->name(), call_def);

      StrEq("input_tensor:0"));

  // Match the `_Retval` node that corresponds to the return value of @main.
  const auto retval_node_itr =
      llvm::find_if(exported_model->graph_def().node(),
                    [](const NodeDef& node) { return node.op() == "_Retval"; });
  ASSERT_NE(retval_node_itr, exported_model->graph_def().node().end());

    return false;
  }

  // _Arg nodes in a top-level function represent feeds and _Retval nodes in a
  // top-level function represent fetches.
  if (stack_depth == 1 &&
      (node.type_string() == "_Arg" || node.type_string() == "_Retval")) {
    absl::string_view uncompilable_reason = "top level _Arg or _Retval";
    MaybeMarkUncompilableNode(uncompilable_reason, *stack_trace,

    if (mul) {
      auto c = ops::Mul(scope.WithOpName("C"), a, b);
      auto d = ops::_Retval(scope.WithOpName("D"), c, 0);
      TF_RETURN_IF_ERROR(scope.ToGraph(graph.get()));
    } else {
      auto c = ops::Add(scope.WithOpName("C"), a, b);
      auto d = ops::_Retval(scope.WithOpName("D"), c, 0);
      TF_RETURN_IF_ERROR(scope.ToGraph(graph.get()));
    }

  auto a = ops::_Arg(scope.WithOpName("A"), DT_INT32, 0);
  auto b = ops::_Arg(scope.WithOpName("B"), DT_INT32, 1);
  auto c = ops::Add(scope.WithOpName("C"), a, b);
  auto d = ops::_Retval(scope.WithOpName("D"), c, 0);
  TF_RETURN_IF_ERROR(scope.ToGraph(graph.get()));
  return graph;
}

absl::StatusOr<FunctionDef> SampleFuntionAddXY(const std::string& name) {

static const char kImportModelDefaultGraphFuncName[] = "main";

// Please refer to the TFG dialect description for the list of used attributes.
// Belows are the attributes in TFE.
// TFE Arguments and Results (Got from "_Arg",
// "_Retval", .etc)
//  NodeDef.device <-> "tf.device"
//  NodeDef.attr <-> "tf."
//
// TFE general operations
//  NodeDef.device <-> "device"
//
// The following two functions are only used for mapping/excluding attributes