code: def test_from_filepaths_multilabel(tmpdir):
      tmpdir = Path(tmpdir)
  
      (tmpdir / "a").mkdir()
      _rand_image().save(tmpdir / "a1.png")
      _rand_image().save(tmpdir / "a2.png")
  
      train_images = [str(tmpdir / "a1.png"), str(tmpdir / "a2.png")]
      train_labels = [[1, 0, 1, 0], [0, 0, 1, 1]]
      valid_labels = [[1, 1, 1, 0], [1, 0, 0, 1]]
      test_labels = [[1, 0, 1, 0], [1, 1, 0, 1]]
  
      dm = AudioClassificationData.from_files(
          train_files=train_images,
          train_targets=train_labels,
          val_files=train_images,
          val_targets=valid_labels,
          test_files=train_images,
          test_targets=test_labels,
          batch_size=2,
          num_workers=0,
      )
  
      data = next(iter(dm.train_dataloader()))
      imgs, labels = data["input"], data["target"]
      assert imgs.shape == (2, 3, 128, 128)
      assert labels.shape == (2, 4)
  
      data = next(iter(dm.val_dataloader()))
      imgs, labels = data["input"], data["target"]
      assert imgs.shape == (2, 3, 128, 128)
      assert labels.shape == (2, 4)
      torch.testing.assert_allclose(labels, torch.tensor(valid_labels))
  
      data = next(iter(dm.test_dataloader()))
      imgs, labels = data["input"], data["target"]
      assert imgs.shape == (2, 3, 128, 128)
      assert labels.shape == (2, 4)
      torch.testing.assert_allclose(labels, torch.tensor(test_labels))
  
imports: from pathlib import Path
  from typing import Tuple
  import flash
  import numpy
  import pytest
  import torch
  from flash.audio import AudioClassificationData
  from flash.core.utilities.imports import _MATPLOTLIB_AVAILABLE, _PIL_AVAILABLE, _TOPIC_AUDIO_AVAILABLE
  from pytorch_lightning import seed_everything
label_desc: Use image node_id 12 for calling a global function with example usage: test_from_filepaths_multilabel(tmpdir) without return types