OpDef op_def;
  op_def.ParseFromString(data);
  EXPECT_EQ(op_def.name(), func_name_);
  EXPECT_EQ(op_def.input_arg_size(), 1);
  EXPECT_EQ(op_def.output_arg_size(), 1);
  EXPECT_FALSE(op_def.is_stateful());

  TF_DeleteBuffer(buffer);
}

void DefineStatefulFunction(const char* name, TF_Function** func) {
  std::unique_ptr<TF_Graph, decltype(&TF_DeleteGraph)> func_graph(

        "//tensorflow/lite/toco:tooling_util",
        "//tensorflow/lite/toco:types_proto_cc",
        "//tensorflow/lite:framework",
        "//tensorflow/lite:shared_library",
        "//tensorflow/lite:stateful_error_reporter",
        "//tensorflow/lite:string_util",
        "//tensorflow/lite:util",
        "//tensorflow/python/grappler:cost_analyzer_lib",
        "//tensorflow/tools/graph_transforms:transform_graph_lib",

        number). These 3 APIs will allow user to disable and switch back to
        legacy behavior if they prefer. In future (eg TF 2.10), we expect to
        totally remove the legacy code path (stateful random Ops), and these 3
        APIs will be removed as well.
    *   `tf.keras.callbacks.experimental.BackupAndRestore` is now available as
        `tf.keras.callbacks.BackupAndRestore`. The experimental endpoint is

// argument does not support this attribute type.
TF_CAPI_EXPORT extern const char* TF_GetNumberAttrForOpListInput(
    const char* op_name, int input_index, TF_Status* status);

// Returns 1 if the op is stateful, 0 otherwise. The return value is undefined
// if the status is not ok.
TF_CAPI_EXPORT extern int TF_OpIsStateful(const char* op_type,
                                          TF_Status* status);

  status->status =
      tensorflow::OpRegistry::Global()->LookUp(op_type, &op_reg_data);
  if (!status->status.ok()) {
    return 0;
  }
  return op_reg_data->op_def.is_stateful();
}

void TF_InitMain(const char* usage, int* argc, char*** argv) {
  tensorflow::port::InitMain(usage, argc, argv);
}

int TF_PickUnusedPortOrDie() {

  DCHECK(append_hash_to_fn_name <= 1);
  tensorflow::FunctionDef fdef;
  status->status = tensorflow::GraphToFunctionDef(
      fn_body->graph, fn_name, append_hash_to_fn_name != 0,
      /*set_stateful_from_nodes=*/true,
      /*copy_placeholder_attrs_from_nodes=*/true, body_nodes, input_tensors,
      output_tensors, output_names_vec, control_output_nodes,
      control_output_names_vec, description, &fdef);