}

  // Populate return_nodes
  DCHECK(tf_results->return_nodes.empty());
  tf_results->return_nodes.resize(results.return_nodes.size());
  for (int i = 0; i < results.return_nodes.size(); ++i) {
    tf_results->return_nodes[i] = ToOperation(results.return_nodes[i]);
  }

  // Populate missing unused map keys
  DCHECK(tf_results->missing_unused_key_names.empty());
  DCHECK(tf_results->missing_unused_key_indexes.empty());

                                 const int64_t* dims, int num_dims) {
  DCHECK(index >= 0 && index < shape_list->num_items);
  TF_ShapeAndType& shape = shape_list->items[index];
  DCHECK(shape.dims == nullptr) << "Shape at " << index << " is already set!";
  DCHECK(num_dims >= 0) << "Number of dimensions cannot be negative!";
  shape.num_dims = num_dims;
  shape.dims = new int64_t[num_dims];

  control_output_nodes.reserve(ncontrol_outputs);
  for (int i = 0; i < ncontrol_outputs; ++i) {
    control_output_nodes.push_back(&control_outputs[i]->node);
  }

  // Do the actual function creation.
  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,

  TF_DeviceList* device_list = list.get();

  CHECK_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  const int num_devices = TF_DeviceListCount(device_list);
  LOG(INFO) << "There are " << num_devices << " devices.";
  for (int i = 0; i < num_devices; ++i) {
    const char* device_name = TF_DeviceListName(device_list, i, s);
    CHECK_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);