}
  if (!result.ok()) {
    status->status = result;
    return;
  }

  // Store results in c_outputs[]
  for (int i = 0; i < noutputs; ++i) {
    const Tensor& src = outputs[i];
    if (!src.IsInitialized() || src.NumElements() == 0) {
      c_outputs[i] =
          EmptyTensor(static_cast<TF_DataType>(src.dtype()), src.shape());
      continue;
    }
    c_outputs[i] = TF_TensorFromTensor(src, &status->status);

  TFE_TensorHandle* h1 = TestVariable(ctx, 2.0, task2_name);
  TFE_TensorHandle* h2 = TestVariable(ctx, 3.0, task0_name);

  // Add a sync point to make sure that variables have been initialized
  // before the function execution starts.
  TFE_ContextAsyncWait(ctx, status);
  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  // Pack 3 variable handles into one TFE_TensorHandle.

      "PluggableDevice plugin functionality is not supported on mobile");
  return nullptr;
#else
  TF_Library* lib_handle = new TF_Library;
  static tensorflow::mutex mu(tensorflow::LINKER_INITIALIZED);
  static std::unordered_map<std::string, void*>* loaded_libs =
      new std::unordered_map<std::string, void*>();
  tensorflow::Env* env = tensorflow::Env::Default();
  {
    tensorflow::mutex_lock lock(mu);

         TF_TensorByteSize(theta_tensor));

  // Initialize space for the numerical gradient.
  vector<float> dtheta_approx(num_elems);

  // Get theta shape and store in theta_dims.
  int num_dims = TF_NumDims(theta_tensor);
  vector<int64_t> theta_dims(num_dims);
  GetDims(theta_tensor, theta_dims.data());

  // Initialize auxilary data structures.
  vector<float> thetaPlus_data(num_elems);

    status_ = TF_NewStatus();
    filesystem_ = new TF_Filesystem;
    filesystem_->plugin_filesystem = nullptr;
    // Because different tests requires different setup for filesystem. We
    // initialize filesystem in each testcase.
  }
  void TearDown() override {
    TF_DeleteStatus(status_);
    if (filesystem_->plugin_filesystem != nullptr)
      tf_gcs_filesystem::Cleanup(filesystem_);
    delete filesystem_;

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

  // Create a variable handle (uninitialized to start) placed on the parallel
  // device.
  std::function<void(Variable*)> variable_deleter = [&](Variable* to_delete) {
    to_delete->Destroy(context, status.get());

}

bool TF_CoordinationServiceIsInitialized(TF_CoordinationServiceAgent* agent) {
  if (agent == nullptr) return false;
  auto* cc_agent = reinterpret_cast<tsl::CoordinationServiceAgent*>(agent);
  return cc_agent->IsInitialized();
}

void TF_CoordinationServiceInsertKeyValue(const char* key, int64_t key_size,
                                          const char* value, int64_t value_size,

    calls++;
    memset(buffer, 'x', n);
    TF_SetStatus(status, TF_OK, "");
    return n;
  };
  std::vector<char> out;
  // Our fake environment is initialized with the current timestamp.
  std::unique_ptr<NowSecondsEnv> env(new NowSecondsEnv);
  uint64 now = Env::Default()->NowSeconds();
  env->SetNowSeconds(now);
  tf_gcs_filesystem::RamFileBlockCache cache(

  //
  // 1. Create a variable on `remote_device`, using `ctx_0`.
  TFE_TensorHandle* handle_0 =
      CreateVariable(ctx_0, 1.2, remote_device, /*variable_name=*/"var");

  // 2. Wait for `var` to be created and initialized on the worker.
  TF_Status* status = TF_NewStatus();
  TFE_ContextAsyncWait(ctx_0, status);
  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TF_DeleteStatus(status);

  // 1. Create a variable on `remote_device`, using `ctx_0`.
  TFE_TensorHandle* handle_0 =
      CreateVariable(ctx_0, 1.2, remote_device, /*variable_name=*/"var2");

  // 2. Wait for `var2` to be created and initialized on the worker.
  TF_Status* status = TF_NewStatus();
  TFE_ContextAsyncWait(ctx_0, status);
  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TF_DeleteStatus(status);