code: def test_trainer_config_device_ids(
      devices, expected_device_ids, tpu_available, monkeypatch
  ):
      monkeypatch.setattr(
          lightning.fabric.accelerators.xla, "_using_pjrt", lambda: True
      )
  
      mock = DeviceMock()
      monkeypatch.setattr(torch, "device", mock)
      if _IS_WINDOWS:
          # simulate fork support on windows
          monkeypatch.setattr(
              torch.multiprocessing,
              "get_all_start_methods",
              lambda: ["fork", "spawn"],
          )
  
      trainer = Trainer(accelerator="tpu", devices=devices)
      assert mock.mock_calls == [
          call("xla", i) for i in expected_device_ids
      ]
      assert len(trainer.device_ids) == len(expected_device_ids)
      assert trainer.num_devices == len(expected_device_ids)
  
imports: import collections
  import os
  from copy import deepcopy
  from unittest import mock
  from unittest.mock import MagicMock, call, patch
  import lightning.fabric
  import pytest
  import torch
  from lightning.fabric.utilities.imports import _IS_WINDOWS
  from lightning.pytorch import Trainer
  from lightning.pytorch.accelerators import CPUAccelerator, XLAAccelerator
  from lightning.pytorch.demos.boring_classes import BoringModel, RandomDataset
  from lightning.pytorch.plugins import Precision, XLACheckpointIO, XLAPrecision
  from lightning.pytorch.strategies import DDPStrategy, XLAStrategy
  from lightning.pytorch.utilities import find_shared_parameters
  from torch import nn
  from torch.utils.data import DataLoader
  from tests_pytorch.helpers.runif import RunIf
  from tests_pytorch.trainer.connectors.test_accelerator_connector import DeviceMock
  from tests_pytorch.trainer.optimization.test_manual_optimization import assert_emtpy_grad
label_desc: Use image node_id 15 for calling a global function with example usage: test_trainer_config_device_ids(devices, expected_device_ids, tpu_available, monkeypatch) without return types