const int last_node_id = graph->graph.num_node_ids();
  tensorflow::ImportGraphDefResults results;
  status->status = tensorflow::ImportGraphDef(opts->opts, def, &graph->graph,
                                              &graph->refiner, &results);
  if (!status->status.ok()) return;

  // Add new nodes to name_map
  for (int i = last_node_id; i < graph->graph.num_node_ids(); ++i) {
    auto* node = graph->graph.FindNodeId(i);

  std::vector<int64_t> source_tensor_ids = MakeTensorIDList(sources);
  tensorflow::gtl::FlatSet<int64_t> sources_set(source_tensor_ids.begin(),
                                                source_tensor_ids.end());
  std::unordered_map<int64_t, TapeTensor> sources_that_are_targets;
  for (int i = 0; i < target_tensor_ids.size(); ++i) {
    int64_t target_id = target_tensor_ids[i];

// once (i.e., on the main function side) in running distributed functions.
// This test creates a cluster with two workers, create a variable on the
// second worker, and run a distributed function (VariableAddFunction) whose ops
// span the local and remote workers. If the graph optimization pass is executed
// on both the main function side and the component function side, an error will

std::unique_ptr<ParallelTensor> ParallelDevice::DeviceIDs(
    TFE_Context* context, TF_Status* status) const {
  std::vector<int32_t> ids;
  ids.reserve(num_underlying_devices());
  for (int i = 0; i < num_underlying_devices(); ++i) {
    ids.push_back(i);
  }
  return ScalarsFromSequence<int32_t>(ids, context, status);
}

absl::optional<std::vector<std::unique_ptr<ParallelTensor>>>
ParallelDevice::Execute(TFE_Context* context,