/*static*/ Status MarkForCompilationPassTestHelper::MarkForCompilation(
    std::unique_ptr<Graph>* graph,
    MarkForCompilationPassTestHelper::Options options) {
  FunctionDefLibrary flib;
  FunctionLibraryDefinition flib_def((*graph)->op_registry(), flib);
  return MarkForCompilation(graph, &flib_def, options);
}

  if (opts.HaveError()) {
    return nullptr;
  }

  NodeBuilder node_builder(name, "Stage", opts.op_registry());
  node_builder.Input(values);
  return opts.FinalizeBuilder(&node_builder);
}

TEST(XlaCompilationTest, StagePipelinePreserved) {
  std::unique_ptr<Graph> graph(new Graph(OpRegistry::Global()));
  {
    // Graph:
    //       b
    //       |
    //       v

};

struct TF_OperationDescription {
  TF_OperationDescription(TF_Graph* g, const char* op_type,
                          const char* node_name)
      : node_builder(node_name, op_type, g->graph.op_registry()), graph(g) {}

  tensorflow::NodeBuilder node_builder;
  TF_Graph* graph;
  std::set<tensorflow::string> colocation_constraints;
};

struct TF_Operation {
  tensorflow::Node node;

 private:

      continue;
    }

    DeviceType device_type("");
    TF_RETURN_IF_ERROR(
        DeviceNameToDeviceType(n->assigned_device_name(), &device_type));
    TF_RETURN_IF_ERROR(MemoryTypesForNode(graph.op_registry(), device_type,
                                          n->def(), &input_mtypes,
                                          &output_mtypes));
    for (const Edge* e : n->out_edges()) {
      Node* dst = e->dst();

Scope Scope::NewRootScope() {
  Graph* graph = new Graph(OpRegistry::Global());
  ShapeRefiner* refiner =
      new ShapeRefiner(graph->versions(), graph->op_registry());
  return Scope(new Impl(graph, new Status, new Impl::NameMap, refiner,
                        /* disable_shape_inference */ false));
}

Scope Scope::DisabledShapeInferenceScope() {
  Graph* graph = new Graph(OpRegistry::Global());
  ShapeRefiner* refiner =

  add2.node()->AddAttr("_cluster", "cluster2");
  auto out = ops::Mul(root.WithOpName("mul"), add1, add2);

  Graph graph_before_encapsulation(OpRegistry::Global());
  TF_ASSERT_OK(root.ToGraph(&graph_before_encapsulation));

  FunctionLibraryDefinition library(OpRegistry::Global(), FunctionDefLibrary());
  std::unique_ptr<Graph> graph;
  TF_ASSERT_OK(EncapsulateSubgraphsInFunctions(
      "_cluster", graph_before_encapsulation,

Status BuildXlaOps(const Scope& s, const FunctionDefLibrary& fdef_lib,
                   std::unique_ptr<Graph>* result) {
  auto graph = std::make_unique<Graph>(OpRegistry::Global());
  TF_RETURN_IF_ERROR(s.ToGraph(graph.get()));
  FunctionLibraryDefinition flib_def(graph->op_registry(), fdef_lib);

  // Assign all nodes to the CPU device.
  static const char* kCpuDevice = "/job:localhost/replica:0/task:0/cpu:0";
  for (Node* n : graph->nodes()) {

          .Attr("Tout", {DT_INT32})
          .Finalize(root.graph(), &if_node));
  std::unique_ptr<Graph> graph(new Graph(OpRegistry::Global()));
  TF_ASSERT_OK(root.ToGraph(graph.get()));

  flib_def_.reset(new FunctionLibraryDefinition(OpRegistry::Global(), flib));

  auto if_node_it = std::find_if(
      graph->nodes().begin(), graph->nodes().end(),

                   const tensorflow::FunctionLibraryDefinition* fnlib_def,
                   GraphShapeInfo* shape_info) {
  ShapeRefiner shape_refiner(graph->versions(), graph->op_registry());
  shape_refiner.set_require_shape_inference_fns(false);
  // TODO(dlibenzi): Verify if it is worth trying to infer shaped within
  // functions. Some functions can be called at multiple locations with

  std::unique_ptr<Graph> graph(new Graph(OpRegistry::Global()));
  {
    GraphDefBuilder builder(GraphDefBuilder::kFailImmediately);
    Node* a =
        ops::SourceOp("UncompilableNullary", builder.opts().WithName("A"));

    // Builds a Symbolic gradient for Supported
    NodeBuilder b_builder("B", "SymbolicGradient",
                          builder.opts().op_registry());
    NameAttrList b_name_attr;