namespace {
typedef std::unique_ptr<TF_Function, decltype(&TF_DeleteFunction)>
    UniqueFuncPtr;
}

// struct TF_Operation { tensorflow::Node node; };
static TF_Operation* ToTF_Operation(Node* node) {
  return static_cast<TF_Operation*>(static_cast<void*>(node));
}

void TF_EnableXLACompilation(TF_SessionOptions* options, unsigned char enable) {
  tensorflow::ConfigProto& config = options->options.config;
  auto* optimizer_options =

TEST(CAPI, DataTypeEnum) {
  EXPECT_EQ(TF_FLOAT, static_cast<TF_DataType>(tensorflow::DT_FLOAT));
  EXPECT_EQ(TF_DOUBLE, static_cast<TF_DataType>(tensorflow::DT_DOUBLE));
  EXPECT_EQ(TF_INT32, static_cast<TF_DataType>(tensorflow::DT_INT32));
  EXPECT_EQ(TF_UINT8, static_cast<TF_DataType>(tensorflow::DT_UINT8));
  EXPECT_EQ(TF_INT16, static_cast<TF_DataType>(tensorflow::DT_INT16));
  EXPECT_EQ(TF_INT8, static_cast<TF_DataType>(tensorflow::DT_INT8));

// New Graph and Session API

// Helper functions -----------------------------------------------------------

namespace {

TF_Operation* ToOperation(Node* node) {
  return static_cast<TF_Operation*>(static_cast<void*>(node));
}

string OutputName(const TF_Output& output) {
  return StrCat(output.oper->node.name(), ":", output.index);
}

const tensorflow::AttrValue* GetAttrValue(TF_Operation* oper,

                         TF_Status* status) {
  tensorflow::FunctionDefLibrary lib;
  {
    tensorflow::mutex_lock l(g->mu);
    lib = g->graph.flib_def().ToProto();
  }
  const auto len = std::min(max_func, static_cast<int>(lib.function_size()));
  for (int i = 0; i < len; ++i) {
    TF_Function* func = new TF_Function();
    func->record = new tensorflow::FunctionRecord(lib.function(i), {}, false);
    funcs[i] = func;
  }

      ASSERT_EQ(sizeof(int32_t), TF_TensorByteSize(out));
      int32_t* output_contents = static_cast<int32_t*>(TF_TensorData(out));
      EXPECT_EQ(expected_results[i], *output_contents);
    }
  }

  void CompareInt32Tensor(const std::vector<int32_t>& expected, TF_Tensor* t) {
    int32_t* data = static_cast<int32_t*>(TF_TensorData(t));
    size_t size = TF_TensorByteSize(t);

        *   Use the free functions such as
            `tensorflow::errors::IsInvalidArgument` if needed.
        *   In the last resort, use
            e.g.`static_cast<tensorflow::errors::Code>(error::Code::INVALID_ARGUMENT)`
            or `static_cast<int>(code)` for comparisons.
*   `tensorflow::StatusOr` will also become in the future an alias to
    `absl::StatusOr`, so use `StatusOr::value` instead of