#if defined(IS_MOBILE_PLATFORM) || defined(IS_SLIM_BUILD)
  status->status = tensorflow::errors::Unimplemented(
      "Server functionality is not supported on mobile");
#else
  status->status = server->server->Start();
#endif  // defined(IS_MOBILE_PLATFORM) || defined(IS_SLIM_BUILD)
}

void TF_ServerStop(TF_Server* server, TF_Status* status) {
#if defined(IS_MOBILE_PLATFORM) || defined(IS_SLIM_BUILD)

  ASSERT_TRUE(
      factory->NewServer(server_def, ServerFactory::Options(), &w0).ok());
  ASSERT_TRUE(w0->Start().ok());
  server_def.set_task_index(1);
  std::unique_ptr<tensorflow::ServerInterface> w1;
  ASSERT_TRUE(
      factory->NewServer(server_def, ServerFactory::Options(), &w1).ok());
  ASSERT_TRUE(w1->Start().ok());

  TF_Status* status = TF_NewStatus();
  TFE_ContextOptions* opts = TFE_NewContextOptions();

                  .ok());
  ASSERT_TRUE(worker_server1->Start().ok());
  server_def_0.set_task_index(2);
  std::unique_ptr<tensorflow::GrpcServer> worker_server2;
  ASSERT_TRUE(tensorflow::GrpcServer::Create(
                  server_def_0, tensorflow::Env::Default(), &worker_server2)
                  .ok());
  ASSERT_TRUE(worker_server2->Start().ok());

  TFE_Context* ctx_0 = CreateContext(serialized_server_def_0,

  TF_DeleteExecutionContext(ctx);
}

TEST_P(UnifiedCAPI, TestBasicGraph) {
  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
      TF_NewStatus(), TF_DeleteStatus);

  // Start a new function / execution context.
  string fn_name = "double";
  TF_ExecutionContext* graph_ctx =
      TF_CreateFunction(fn_name.c_str(), status.get());

namespace gradients {

using namespace std;

// ================== Helper functions =================

// Fills data with values [start,end) with given step size.
void Range(vector<int32_t>* data, int32_t start, int32_t end,
           int32_t step = 1) {
  for (int32_t i = start; i < end; i += step) {
    (*data)[i] = i;
  }
}

// Fills out_dims with the dimensions of the given tensor.

                  .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)
                  .ok());
  ASSERT_TRUE(worker_server2->Start().ok());

  TF_Status* status = TF_NewStatus();

  tensorflow::mutex execution_mutex_;
  ExecutionState execution_state_ TF_GUARDED_BY(execution_mutex_) =
      ExecutionState::kIdle;
  // Tells the worker thread that there is new work.
  tensorflow::condition_variable start_execute_;
  // The worker thread notifies that work has finished.
  tensorflow::condition_variable finished_execute_;
  // Notifies a StartExecute that the previous Join has finished.

    absl::MutexLock l(&gcs_file->buffer_mutex);
    size_t buffer_end = gcs_file->buffer_start + gcs_file->buffer.size();
    size_t copy_size = 0;
    if (offset < buffer_end && gcs_file->buffer_start) {
      copy_size = (std::min)(n, static_cast<size_t>(buffer_end - offset));
      memcpy(buffer,
             gcs_file->buffer.data() + (offset - gcs_file->buffer_start),
             copy_size);
    }
    bool consumed_buffer_to_eof =

              {"add2:sum:0", "final_out"}},
             {});
}

TEST_F(CApiFunctionTest, ImportFunctionDef_InvalidProto) {
  // Invalid protobuf data (protos cannot start with 4 bytes of zeros)
  char proto[] = {0x0, 0x0, 0x0, 0x0};
  func_ = TF_FunctionImportFunctionDef(proto, 4, s_);
  EXPECT_TRUE(func_ == nullptr);
  EXPECT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(s_));