code: def test_model_parameters_on_device_for_optimizer(strategy):
      """Test that the strategy has moved the parameters to the device by the time the optimizer gets created."""
      model = CheckOptimizerDeviceModel()
      trainer = Trainer(
          default_root_dir=os.getcwd(),
          fast_dev_run=1,
          accelerator="gpu",
          devices=1,
          strategy=strategy,
      )
      trainer.fit(model)
  
imports: import os
  from unittest import mock
  from unittest.mock import Mock
  import lightning.pytorch
  import pytest
  import torch
  from lightning.fabric.plugins.environments import ClusterEnvironment, LightningEnvironment
  from lightning.fabric.utilities.distributed import _distributed_is_initialized
  from lightning.fabric.utilities.imports import _TORCH_GREATER_EQUAL_2_0
  from lightning.pytorch import Trainer
  from lightning.pytorch.callbacks import Callback, EarlyStopping
  from lightning.pytorch.demos.boring_classes import BoringDataModule, BoringModel
  from lightning.pytorch.strategies import DDPStrategy
  from lightning.pytorch.strategies.launchers import _SubprocessScriptLauncher
  from lightning.pytorch.strategies.launchers.multiprocessing import _MultiProcessingLauncher
  from lightning.pytorch.trainer import seed_everything
  from torch.distributed.optim import ZeroRedundancyOptimizer
  from torch.multiprocessing import ProcessRaisedException
  from torch.nn.parallel.distributed import DistributedDataParallel
  import tests_pytorch.helpers.pipelines
  from tests_pytorch.helpers.datamodules import ClassifDataModule
  from tests_pytorch.helpers.runif import RunIf
  from tests_pytorch.helpers.simple_models import ClassificationModel
label_desc: Use image node_id 15 for calling a global function with example usage: test_model_parameters_on_device_for_optimizer(strategy) without return types