NameAttrList function;
  function.set_name(std::string{func_name});

  std::string device_name;
  if (!platform_name.empty()) {
    device_name = absl::StrCat("/device:", platform_name, ":0");
  }
  FunctionLibraryRuntime* flr = pflr->GetFLR(device_name);
  if (flr == nullptr) {
    // Use CPU as the fallback to get the `FunctionLibraryRuntime`.
    flr = pflr->GetFLR("/device:CPU:0");

  }

  using DeviceTypeConstRef = std::reference_wrapper<const DeviceType>;

  absl::StatusOr<DeviceTypeConstRef> GetDeviceTypeFor(
      absl::string_view device_name) {
    TF_ASSIGN_OR_RETURN(DeviceId device_id, GetIdFor(device_name));
    return std::cref(*id_to_device_type_[device_id.id()]);
  }

  string DebugString(const DeviceSet& device_set) const;

 private:

  const std::string attrKey = "T";
  for (const Node* node : graph.nodes()) {
    if (node->type_string() == nodeType) {
      auto device_name = node->assigned_device_name();
      DeviceNameUtils::ParsedName device;
      if (DeviceNameUtils::ParseFullName(device_name, &device) &&
          device.has_job && device.job == jobType) {
        for (const auto& attr : node->attrs()) {
          auto attr_key = attr.first;

    return absl::OkStatus();
  }

  int device_count = platform->VisibleDeviceCount();

  for (int i = 0; i < device_count; ++i) {
    const string device_name = absl::StrCat("/physical_device:TPU:", i);
    devices->push_back(device_name);
  }

  return absl::OkStatus();
}

Status TpuNodeDeviceFactory::CreateDevices(
    const SessionOptions& session_options, const string& name_prefix,

                      se::PlatformManager::PlatformWithName("Host"));

  XlaDevice::Options options;
  options.platform = platform;
  options.device_name_prefix = name_prefix;
  options.device_name = DEVICE_XLA_CPU;
  options.device_ordinal = 0;
  options.compilation_device_name = DEVICE_CPU_XLA_JIT;
  options.use_multiple_streams = false;
  XlaShapeLayoutHelpers::ShapeDeterminationFns shape_representation_fns{

    const char* node_name = opdef.name().c_str();
    const char* op_name = opdef.name().c_str();
    const char* device_name = "CPU";
    static auto fake_compute_func = [](void* kernel, TF_OpKernelContext* ctx) {
    };

    TF_KernelBuilder* builder =
        TF_NewKernelBuilder(op_name, device_name, /*create_func=*/nullptr,
                            fake_compute_func, /*delete_func=*/nullptr);

                                                         TF_Status* status);

// Create a new TFE_TensorHandle with the same contents as 'h' but placed
// in the memory of the device name 'device_name'.
// If source and destination are the same device, then this creates a new handle
// that shares the underlying buffer. Otherwise, it currently requires at least

      gpu_ids->insert(i);
    }
  }
  for (int i : *gpu_ids) {
    XlaDevice::Options options;
    options.platform = platform.value();
    options.device_name_prefix = name_prefix;
    options.device_name = DEVICE_XLA_GPU;
    options.device_ordinal = i;
    options.compilation_device_name = DEVICE_GPU_XLA_JIT;
    options.use_multiple_streams = true;
    options.allowed_devices = gpu_ids;

  if (!device || device.getValue().empty()) {
    return std::nullopt;
  }
  const std::string device_name = device.str();
  tensorflow::DeviceNameUtils::ParsedName parsed_name;
  if (!tensorflow::DeviceNameUtils::ParseFullName(device_name, &parsed_name)) {
    return std::nullopt;
  }
  if (!parsed_name.has_type) {
    return std::nullopt;
  }
  return parsed_name.type;
}

// ParallelDevice.
void DeleteParallelDevice(void* device_info) {
  delete reinterpret_cast<NamedParallelDevice*>(device_info);
}

}  // namespace

void AllocateParallelDevice(const char* device_name,
                            const char* const* underlying_devices,
                            int num_underlying_devices,
                            TFE_CustomDevice* device, void** device_info) {