TF_SessionOptions* opts = TF_NewSessionOptions();
  TF_Session* sess = TF_NewSession(graph, opts, s);
  TF_DeleteSessionOptions(opts);

  TF_Output feeds[] = {TF_Output{a, 0}, TF_Output{b, 0}};
  TF_Output fetches[] = {TF_Output{plus2, 0}, TF_Output{plusB, 0}};

  const char* handle = nullptr;
  TF_SessionPRunSetup(sess, feeds, TF_ARRAYSIZE(feeds), fetches,

  cond_graph->parent_inputs = inputs;
  body_graph->parent = g;
  body_graph->parent_inputs = inputs;

  TF_Output* cond_inputs = new TF_Output[ninputs];
  TF_Output cond_output = {nullptr, -1};
  TF_Output* body_inputs = new TF_Output[ninputs];
  TF_Output* body_outputs = new TF_Output[ninputs];
  for (int i = 0; i < ninputs; ++i) body_outputs[i] = {nullptr, -1};
  const char* name = nullptr;

  TF_Graph* const cond_graph;
  const TF_Output* const cond_inputs;
  TF_Output cond_output;

  // The loop body graph. The inputs are the current values of the loop
  // variables. The outputs are the updated values of the loop variables.
  TF_Graph* const body_graph;
  const TF_Output* const body_inputs;
  TF_Output* const body_outputs;

TF_Function* TF_GraphToFunctionWithControlOutputs(
    const TF_Graph* fn_body, const char* fn_name,
    unsigned char append_hash_to_fn_name, int num_opers,
    const TF_Operation* const* opers, int ninputs, const TF_Output* inputs,
    int noutputs, const TF_Output* outputs, const char* const* output_names,
    int ncontrol_outputs, const TF_Operation* const* control_outputs,
    const char* const* control_output_names, const TF_FunctionOptions* opts,

auto& kUnknownRank = shape_inference::InferenceContext::kUnknownRank;

// GraphTensor wraps a `TF_Output`, i.e. a pointer to TF_Operation and the index
// into the list of outputs for the operation.
class GraphTensor : public TracingTensorHandle {
 public:
  explicit GraphTensor(TF_Output output, TF_Graph* graph)
      : TracingTensorHandle(kGraph), output_(output), graph_(graph) {}

    for (int i = 0; i < expected.size(); ++i) {
      ASSERT_EQ(expected[i], data[i]) << "Different data at index " << i;
    }
  }

  std::vector<TF_Output> ToOutput(const std::vector<TF_Operation*> ops) {
    std::vector<TF_Output> out;
    for (auto op : ops) {
      out.push_back({op, 0});
    }
    return out;
  }

  void Define(int num_opers, const std::vector<TF_Operation*>& opers,

  if (dequeue_op == nullptr) {
    status->status = tensorflow::errors::Internal(
        "Unable to find the dequeue node in the TF graph.");
    return nullptr;
  }

  VLOG(1) << "Running the dequeue op";
  TF_Output output{dequeue_op, 0};
  TF_Tensor* ret;
  TF_SessionRun(session, /*run_options*/ nullptr,
                // input related parameters
                /*inputs*/ nullptr, /*input_values*/ nullptr, /*ninputs*/ 0,

  TF_SetAttrType(id_descr, "T", TF_INT32);
  TF_AddInput(id_descr, {arg, 0});
  TF_Operation* id = TF_FinishOperation(id_descr, status);
  ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status);
  TF_Output input{arg, 0};
  TF_Output output{id, 0};
  TF_Function* fn =
      TF_GraphToFunction(function_graph, "ident", 0, 1, &id, 1, &input, 1,
                         &output, nullptr, nullptr, "test", status);

    indicating its preferred use in language bindings for TensorFlow. What was
    previously `TF_Session` has been renamed to `TF_DeprecatedSession`.
*   Renamed `TF_Port` to `TF_Output` in the C API.
*   Removes RegisterShape from public API. Use C++ shape function registration
    instead. indexing now starts from 1 instead of 0, and `bus_id==0` is used
    where previously `BUS_ANY` was used.