graph->ToGraphDef(graphdef.get());
  return graphdef;
}

absl::Status ConvertMlirFunctionToFunctionLibraryDef(
    FuncOp func, const GraphExportConfig& configs, FunctionDef* function_def) {
  Dialect* tf_dialect = func.getContext()->getLoadedDialect("tf");
  FunctionLibraryDefinition flib_def(OpRegistry::Global(),
                                     FunctionDefLibrary());

#include "tensorflow/core/platform/types.h"
#include "tsl/lib/core/status_test_util.h"

namespace tensorflow {

namespace {

// Produce a valid graph with a resource-type input.
FunctionDef PassThroughResource() {
  return FunctionDefHelper::Define(
      /*function_name=*/"PassThroughResource",
      /*arg_def=*/{"in: resource"},
      /*ret_def=*/{"out: resource"},
      /*attr_def=*/{},

                               TF_Buffer* buf, TF_Status* status) {
  auto* function_def = tensorflow::unwrap(ctx)->FindFunctionDef(function_name);
  if (function_def == nullptr) {
    status->status = tensorflow::errors::NotFound(
        "Unable to find FunctionDef with name: ", function_name);
    return;
  }
  string str = function_def->SerializeAsString();
  void* data = tensorflow::port::Malloc(str.length());

namespace tensorflow {
namespace tracing {
namespace graph {
GraphFunction::GraphFunction(FunctionDef fdef)
    : AbstractFunction(kGraph),
      func_record_(new FunctionRecord(std::move(fdef), {}, true)) {}
GraphFunction::~GraphFunction() {}
Status GraphFunction::GetFunctionDef(const FunctionDef **fdef) {
  *fdef = &(func_record_->fdef());
  return absl::OkStatus();
}
}  // namespace graph

    node_name_image["identity1"]->AddAttr("_oc", "1");
    PartialTensorShape shape({2});
    node_name_image["identity1"]->AddAttr(
        kXlaInferredShapesAttrName, std::vector<PartialTensorShape>{shape});

    FunctionDef *xla_fdef = fdl.add_function();
    TF_CHECK_OK(GraphToFunctionDef(*g, "cluster", xla_fdef));
  }
  FunctionLibraryDefinition fld(OpRegistry::Global(), fdl);

  protobuf::Map<string, tensorflow::AttrValue> attrs;

    auto ret0 = ops::_Retval(s.WithOpName("ret0"), arg1, 0);
    auto ret1 = ops::_Retval(s.WithOpName("ret1"), arg0, 1);
    std::unique_ptr<Graph> g(new Graph(OpRegistry::Global()));
    TF_CHECK_OK(s.ToGraph(g.get()));
    FunctionDef *xla_fdef = fdl.add_function();
    TF_CHECK_OK(GraphToFunctionDef(*g, "f1", xla_fdef));
  }
  {
    // Function for While's "body".
    // "arg0" (T=DT_RESOURCE), "arg1" (T=DT_BOOL)
    // "ret0" = "arg0"

struct TFSignatureDefFunctionRevivalState {
  // Index of the node in the SavedObjectGraph it was loaded from.
  int node_id = 0;

  // Pointer to the original functiondef. fdef_ is guaranteed to be
  // non-null.
  const FunctionDef* fdef = nullptr;

  // SavedConcreteFunction contains much of the metadata of the expected "types"
  // of the inputs and outputs of a function.

struct TFConcreteFunctionRevivalState {
  // Index of the node in the SavedObjectGraph it was loaded from.
  int node_id;

  // Pointer to the original functiondef. fdef_ is guaranteed to be
  // non-null.
  const FunctionDef* fdef;

  // TensorHandle captures for this funtion
  std::vector<ImmediateExecutionTensorHandle*> captures;

  op_builder.create<mlir::func::ReturnOp>(loc, tf_op->getResults());

  // Run the decompose passes on the module
  TF_RETURN_IF_ERROR(DecomposeGraph(module));

  // Export the result as a FunctionDef.
  FunctionDef func_def;
  TF_RETURN_IF_ERROR(
      ConvertMlirFunctionToFunctionLibraryDef(func, export_confs_, &func_def));
  module.erase();
  return func_def;
}

TF_Function* TF_FunctionImportFunctionDef(const void* proto, size_t proto_len,
                                          TF_Status* status) {
  tensorflow::FunctionDef fdef;
  bool success = fdef.ParseFromArray(proto, proto_len);
  if (!success) {
    status->status = InvalidArgument(
        "Invalid FunctionDef given to TF_FunctionImportFunctionDef");
    return nullptr;
  }

  TF_Function* func = new TF_Function();