TF_Graph* graph;
  std::set<tensorflow::string> colocation_constraints;
};

struct TF_Operation {
  tensorflow::Node node;

 private:
  ~TF_Operation() = default;
};

struct TF_Session {
  TF_Session(tensorflow::Session* s, TF_Graph* g);

  tensorflow::Session* session;
  TF_Graph* const graph;

  tensorflow::mutex mu TF_ACQUIRED_AFTER(TF_Graph::mu);
  int last_num_graph_nodes;

  void Run(TF_Status* s);

  void CloseAndDelete(TF_Status* s);

  TF_Tensor* output_tensor(int i) { return output_values_[i]; }

  TF_Session* mutable_session() { return session_; }

 private:
  void DeleteInputValues();
  void ResetOutputValues();

  TF_Session* session_;
  std::vector<TF_Output> inputs_;
  std::vector<TF_Tensor*> input_values_;
  std::vector<TF_Output> outputs_;

}

void UpdateEdge(TF_Graph* graph, TF_Output new_src, TF_Input dst,
                TF_Status* status) {
  TF_UpdateEdge(graph, new_src, dst, status);
}

void ExtendSession(TF_Session* session, TF_Status* status) {
  TF_ExtendSession(session, status);
}

std::string GetHandleShapeAndType(TF_Graph* graph, TF_Output output) {
  Node* node = &output.oper->node;

#endif  // defined(IS_MOBILE_PLATFORM) || defined(IS_SLIM_BUILD)
}

// TF_Session functions ----------------------------------------------

TF_Session::TF_Session(tensorflow::Session* s, TF_Graph* g)
    : session(s), graph(g), last_num_graph_nodes(0), extend_before_run(true) {}

TF_Session* TF_NewSession(TF_Graph* graph, const TF_SessionOptions* opt,
                          TF_Status* status) {

// API for driving Graph execution.

typedef struct TF_Session TF_Session;

// Return a new execution session with the associated graph, or NULL on
// error. Does not take ownership of any input parameters.
//
// *`graph` must be a valid graph (not deleted or nullptr). `graph` will be
// kept alive for the lifetime of the returned TF_Session. New nodes can still
// be added to `graph` after this call.

// above mutation methods. This allows us to prevent modifications to nodes in
// the graph after the session has been made aware of them.
void ExtendSession(TF_Session* session, TF_Status* status);

// Returns the serialized CppShapeInferenceResult::HandleData proto for
// `output` if its a resource or variant tensor, or otherwise returns the empty
// string.

// empty, this call is blocked.
//
// Tensors are enqueued via the corresponding TF enqueue op.
// TODO(hongm): Add support for `timeout_ms`.
TF_CAPI_EXPORT extern TF_Tensor* TF_DequeueNamedTensor(TF_Session* session,
                                                       int tensor_id,
                                                       TF_Status* status);

    if (!status->status.ok()) return {};
    ret.push_back(UniqueFuncPtr(func, TF_DeleteFunction));
  }
  return ret;
}

TF_Tensor* TF_DequeueNamedTensor(TF_Session* session, int tensor_id,
                                 TF_Status* status) {
  assert(session);
  {
    tensorflow::mutex_lock c(session->graph->mu);
    VLOG(1) << "Dequeuing named tensor with id " << tensor_id

      << "'" << s << "' does not contain '" << expected << "'";
}

// Returns the GPU device name if there is one (with arbitrary tie breaking if
// there are more than one), or "" otherwise.
string GPUDeviceName(TF_Session* session) {
  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
      TF_NewStatus(), TF_DeleteStatus);
  TF_Status* s = status.get();
  std::unique_ptr<TF_DeviceList, decltype(&TF_DeleteDeviceList)> list(

  TF_SessionOptions* opts = TF_NewSessionOptions();
  TF_EnableXLACompilation(opts, use_XLA);
  session_ = TF_NewSession(graph, opts, s);
  TF_DeleteSessionOptions(opts);
}

CSession::CSession(TF_Session* session) : session_(session) {}

CSession::~CSession() {
  TF_Status* s = TF_NewStatus();
  CloseAndDelete(s);
  EXPECT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
  TF_DeleteStatus(s);
}