ASSERT_TRUE(tensorflow::GrpcServer::Create(
                  server_def, tensorflow::Env::Default(), &worker_server1)
                  .ok());
  ASSERT_TRUE(worker_server1->Start().ok());

  server_def.set_task_index(2);
  std::unique_ptr<tensorflow::GrpcServer> worker_server2;
  ASSERT_TRUE(tensorflow::GrpcServer::Create(
                  server_def, tensorflow::Env::Default(), &worker_server2)

  // When creating file, use the same permissions as original
  mode_t open_mode = mode & (S_IRWXU | S_IRWXG | S_IRWXO);

  // O_WRONLY | O_CREAT | O_TRUNC:
  //   Open file for write and if file does not exist, create the file.
  //   If file exists, truncate its size to 0.
  int dst_fd = open(dst, O_WRONLY | O_CREAT | O_TRUNC, open_mode);
  if (dst_fd < 0) {
    close(src_fd);
    return -1;
  }

                                 export_dir, tag_set, &bundle);
  if (!status->status.ok()) return nullptr;

  // Create a TF_Graph from the MetaGraphDef. This is safe as long as Session
  // extends using GraphDefs. The Graph instance is different, but equivalent
  // to the one used to create the session.
  //
  // TODO(jhseu): When Session is modified to take Graphs instead of

using tensorflow::tracing::TracingTensorHandle;

void TF_SetTracingImplementation(const char* name, TF_Status* s) {
  tsl::Set_TF_Status_from_Status(s, SetDefaultTracingEngine(name));
}

// Creates a new TensorFlow function, it is an execution context attached to a
// given tracing context.
TF_ExecutionContext* TF_CreateFunction(const char* fn_name, TF_Status* s) {
  return wrap(CreateTracingExecutionContext(fn_name, s));

      container_name, plugin_resource_name, &tf_plugin_resource,
      [plugin_resource_name, create_func, create_func_args,
       delete_func](tensorflow::PluginResource** new_resource) {
        void* opaque_plugin_resource = create_func(create_func_args);
        *new_resource = new tensorflow::PluginResource(
            opaque_plugin_resource, plugin_resource_name, delete_func);

  ASSERT_TRUE(tensorflow::GrpcServer::Create(
                  server_def, tensorflow::Env::Default(), &worker_server1)
                  .ok());
  ASSERT_TRUE(worker_server1->Start().ok());

  server_def.set_task_index(2);
  std::unique_ptr<tensorflow::GrpcServer> worker_server2;
  ASSERT_TRUE(tensorflow::GrpcServer::Create(
                  server_def, tensorflow::Env::Default(), &worker_server2)

// correspond fairly well to the implementation, but testing the C++ directly is
// another option.

namespace tensorflow {
namespace parallel_device {

Variable* Variable::Create(TFE_Context* context, TF_DataType type,
                           const int64_t* dims, const int num_dims,
                           const char* device, TF_Status* status) {
  std::unique_ptr<TFE_Op, decltype(&TFE_DeleteOp)> op(

  tensorflow::Status s = tensorflow::GrpcServer::Create(
      updated_server_def, tensorflow::Env::Default(), &worker_server);
  ASSERT_TRUE(s.ok()) << s.message();
  ASSERT_TRUE(worker_server->Start().ok());

  TFE_ContextSetServerDef(ctx, 0, serialized.data(), serialized.size(), status);
  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  // Create a new tensor_handle.

  RegisterParallelDevice(context.get(), device_name, underlying_devices,
                         status.get());
  ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());

  // Create two vectors with different lengths
  std::vector<float> size_two_value{1., 2.};
  std::vector<float> size_three_value{1., 2., 3.};
  TensorHandlePtr size_two(
      VectorFloatTensorHandle(size_two_value, status.get()));

  string serialized = server_def.SerializeAsString();

  server_def.set_task_index(1);

  std::unique_ptr<tensorflow::GrpcServer> worker_server;
  ASSERT_TRUE(tensorflow::GrpcServer::Create(
                  server_def, tensorflow::Env::Default(), &worker_server)
                  .ok());
  ASSERT_TRUE(worker_server->Start().ok());

  TF_Status* status = TF_NewStatus();