code: def test_trainer_fit(tmpdir, callbacks, should_warn):
      model = nn.Sequential(
          nn.Flatten(), nn.Linear(28 * 28, 10), nn.LogSoftmax()
      )
      train_dl = DataLoader(DummyDataset())
      val_dl = DataLoader(DummyDataset())
      task = ClassificationTask(model, loss_fn=F.nll_loss)
      trainer = Trainer(
          fast_dev_run=True,
          default_root_dir=tmpdir,
          callbacks=callbacks,
      )
  
      if should_warn:
          with pytest.warns(
              UserWarning,
              match="trainer is using a fine-tuning callback",
          ):
              trainer.fit(task, train_dl, val_dl)
      else:
          trainer.fit(task, train_dl, val_dl)
  
imports: from argparse import ArgumentParser
  from typing import Any, Tuple, Union
  import pytest
  import torch
  from flash import Trainer
  from flash.core.classification import ClassificationTask
  from flash.core.utilities.imports import _TOPIC_CORE_AVAILABLE
  from pytorch_lightning.callbacks.early_stopping import EarlyStopping
  from pytorch_lightning.callbacks.finetuning import BaseFinetuning
  from pytorch_lightning.core.lightning import LightningModule
  from torch import nn
  from torch.nn import functional
  from torch.optim.optimizer import Optimizer
  from torch.utils.data import DataLoader
label_desc: Use image node_id 1 for calling a global function with example usage: test_trainer_fit(tmpdir, callbacks, should_warn) without return types