code: def test_multilabel(tmpdir):
      num_classes = 4
      ds = DummyMultiLabelDataset(num_classes)
      model = ImageClassifier(num_classes, multi_label=True)
      train_dl = torch.utils.data.DataLoader(ds, batch_size=2)
      trainer = Trainer(
          default_root_dir=tmpdir, max_epochs=2, limit_train_batches=5
      )
      trainer.finetune(model, train_dl, strategy=("freeze_unfreeze", 1))
      predictions = trainer.predict(
          model, train_dl, output="probabilities"
      )[0]
      assert (torch.tensor(predictions) > 1).sum() == 0
      assert (torch.tensor(predictions) < 0).sum() == 0
      assert len(predictions[0]) == num_classes
  
imports: from typing import Any
  from unittest.mock import patch
  import pytest
  import torch
  from flash import Trainer
  from flash.core.data.io.input import DataKeys
  from flash.core.utilities.imports import _TOPIC_IMAGE_AVAILABLE, _TOPIC_SERVE_AVAILABLE
  from flash.image import ImageClassifier
  from torch import Tensor
  from tests.helpers.task_tester import TaskTester
label_desc: Use image node_id 4 for calling a global function with example usage: test_multilabel(tmpdir) without return types