}

Node* RetOp(int index, ops::NodeOut a, const GraphDefBuilder::Options& opts) {
  if (opts.HaveError()) return nullptr;
  NodeBuilder node_builder(opts.GetNameForOp("Retval"), "_Retval",
                           opts.op_registry());
  node_builder.Input(std::move(a)).Attr("index", index);
  return opts.FinalizeBuilder(&node_builder);
}

    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()));

//sys	Fchown(fd int, uid int, gid int) (err error)
//sys	Fchownat(fd int, path string, uid int, gid int, flags int) (err error) = SYS___FCHOWNAT_A
//sys	FcntlInt(fd uintptr, cmd int, arg int) (retval int, err error) = SYS_FCNTL
//sys	Fdatasync(fd int) (err error) = SYS_FDATASYNC
//sys	fstat(fd int, stat *Stat_LE_t) (err error)
//sys	fstatat(dirfd int, path string, stat *Stat_LE_t, flags int) (err error) = SYS___FSTATAT_A

                              PatternRewriter &rewriter) {
  if (concat.getVal().size() < 2)
    return rewriter.notifyMatchFailure(
        concat, "Concatenate op should have at least two operands");

  auto first = concat.getVal()[0].getDefiningOp<mhlo::SliceOp>();
  auto second = concat.getVal()[1].getDefiningOp<mhlo::SliceOp>();
  if (!first || !second)

Kuat <******@****.***> 1704828110 -0800

func i64tor(x int64Val) ratVal   { return ratVal{newRat().SetInt64(int64(x))} }
func i64tof(x int64Val) floatVal { return floatVal{newFloat().SetInt64(int64(x))} }
func itor(x intVal) ratVal       { return ratVal{newRat().SetInt(x.val)} }
func itof(x intVal) floatVal     { return floatVal{newFloat().SetInt(x.val)} }
func rtof(x ratVal) floatVal     { return floatVal{newFloat().SetRat(x.val)} }
func vtoc(x Value) complexVal    { return complexVal{x, int64Val(0)} }

    for (auto input : inputs) {
      TF_RETURN_IF_ERROR(fn_op->AddInput(input));
    }
    int retvals = outputs.size() - null_indices.size();
    std::vector<AbstractTensorHandle*> fn_outputs(retvals);
    TF_RETURN_IF_ERROR(fn_op->Execute(
        absl::Span<AbstractTensorHandle*>(fn_outputs.data(), fn_outputs.size()),
        &retvals));
    int skipped_indices = 0;
    for (int i = 0; i < outputs.size(); i++) {

  TFE_TensorHandle* retvals[1] = {nullptr};
  int num_retvals = 1;
  TFE_Execute(func, &retvals[0], &num_retvals, status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  ASSERT_EQ(1, num_retvals);
  TF_Tensor* t = TFE_TensorHandleResolve(retvals[0], status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_DeleteTensorHandle(retvals[0]);
  float sum = 0;

    // CHECK: tf_executor.island wraps "tf._Retval"(%outputs_0) {T = !tf_type.resource, _mlir_name = "func_call", index = 0 : i64} : (tensor<*x!tf_type.resource>) -> ()
    %ctl_1 = _Retval(%test_func_name0) name("func_call") {T = !tf_type.resource, index = 0 : i64} : tensor<*x!tf_type.resource>
    // CHECK: tf_executor.fetch
  }

// CHECK-NEXT:    "tf.IfrtCall"(%arg0, [[ARRAYKEY]]) <{program_id = 6515870160938153680 : i64, variable_arg_indices = [1 : i32]}> {__tpu_compile_metadata_text = "retvals { sharding { } }"} : (tensor<1x3xf32>, tensor<!tf_type.string>) -> tensor<1x1xf32>
// CHECK-NEXT:    return
//
 func.func @serving_default(%arg0: tensor<1x3xf32>) -> tensor<1x1xf32> {