code: def test_if_test_works_after_train(tmpdir):
      """Ensure that .test() works after .fit()"""
      model = BoringModel()
      trainer = Trainer(
          max_epochs=1,
          accelerator="tpu",
          devices="auto",
          default_root_dir=tmpdir,
          fast_dev_run=True,
      )
      trainer.fit(model)
      out = trainer.test(model)
      assert len(out) == 1
  
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 2 for calling a global function with example usage: test_if_test_works_after_train(tmpdir) without return types