code: def test_single_gpu():
      """Tests if device is set correctly when training and after teardown for single GPU strategy.
  
      Cannot run this test on MPS due to shared memory not allowing dedicated measurements of GPU memory utilization.
  
      """
      trainer = Trainer(accelerator="gpu", devices=1, fast_dev_run=True)
      # assert training strategy attributes for device setting
      assert isinstance(trainer.strategy, SingleDeviceStrategy)
      assert trainer.strategy.root_device == torch.device("cuda:0")
  
      model = BoringModelGPU()
  
      trainer.fit(model)
  
      # assert after training, model is moved to CPU and memory is deallocated
      assert model.device == torch.device("cpu")
      cuda_memory = torch.cuda.memory_allocated()
      assert cuda_memory < model.start_cuda_memory
  
imports: import pickle
  from unittest.mock import MagicMock, Mock
  import pytest
  import torch
  from lightning.pytorch import Trainer
  from lightning.pytorch.core.optimizer import LightningOptimizer
  from lightning.pytorch.demos.boring_classes import BoringModel, RandomDataset
  from lightning.pytorch.strategies import SingleDeviceStrategy
  from torch.utils.data import DataLoader
  from tests_pytorch.helpers.dataloaders import CustomNotImplementedErrorDataloader
  from tests_pytorch.helpers.runif import RunIf
label_desc: Use image node_id 2 for calling a global function with example usage: test_single_gpu() without return types