TF_EXCLUSIVE_LOCKS_REQUIRED(desc->graph->mu) {
  Node* ret = nullptr;

  if (desc->graph->name_map.count(desc->node_builder.node_name())) {
    status->status = InvalidArgument("Duplicate node name in graph: '",
                                     desc->node_builder.node_name(), "'");
  } else {
    if (!desc->colocation_constraints.empty()) {
      desc->node_builder.Attr(

                              const char* prefix, TF_Status* status) {
  std::vector<std::unique_ptr<tensorflow::Device>> devices;

  if (prefix == nullptr || strlen(prefix) == 0)
    prefix = "/job:localhost/replica:0/task:0";

  tensorflow::SessionOptions sess_options;
  (*sess_options.config.mutable_device_count())["CPU"] = num_cpus;
  status->status =
      tensorflow::DeviceFactory::AddCpuDevices(sess_options, prefix, &devices);

    return nullptr;
  }
  std::vector<std::unique_ptr<tensorflow::Device>> devices;
  status->status = tensorflow::DeviceFactory::AddDevices(
      opts->session_options.options, "/job:localhost/replica:0/task:0",
      &devices);
  if (!status->status.ok()) return nullptr;
  std::unique_ptr<tensorflow::DeviceMgr> device_mgr(
      new tensorflow::DynamicDeviceMgr(std::move(devices)));

        class to return a `tf.experimental.Optional` instance that contains the
        next value for all replicas or none instead of raising an out of range
        error. Also see *new*
        [guide on input distribution](https://www.tensorflow.org/tutorials/distribute/input).
*   Allow var.assign on MirroredVariables with aggregation=NONE in replica
    context. Previously this would raise an error. We now allow this because