code: def test_finetuning_with_none_return_type(strategy, plugins):
      gpus = 0 if plugins is None else 1
      task = TestTaskWithoutFinetuning(loss_fn=F.nll_loss)
      trainer = flash.Trainer(
          max_epochs=1,
          limit_train_batches=10,
          gpus=gpus,
          plugins=plugins,
      )
      ds = DummyDataset()
      trainer.finetune(
          task, train_dataloader=DataLoader(ds), strategy=strategy
      )
  
imports: from numbers import Number
  from typing import Iterable, List, Optional, Tuple, Union
  import flash
  import pytest
  import pytorch_lightning
  import torch
  from flash.core.finetuning import _FINETUNING_STRATEGIES_REGISTRY
  from flash.core.model import Task
  from flash.core.utilities.imports import _DEEPSPEED_AVAILABLE, _TOPIC_CORE_AVAILABLE
  from pytorch_lightning.callbacks.model_checkpoint import ModelCheckpoint
  from torch import Tensor
  from torch.nn import Flatten, Linear, LogSoftmax, Module
  from torch.nn import functional
  from torch.utils.data import DataLoader
  from tests.helpers.boring_model import BoringModel
label_desc: Use image node_id 1 for calling a global function with example usage: test_finetuning_with_none_return_type(strategy, plugins) without return types