code: def test_from_filepaths_visualise_multilabel(tmpdir):
      tmpdir = Path(tmpdir)
  
      (tmpdir / "a").mkdir()
      (tmpdir / "b").mkdir()
  
      image_a = str(tmpdir / "a" / "a_1.png")
      image_b = str(tmpdir / "b" / "b_1.png")
  
      _rand_image().save(image_a)
      _rand_image().save(image_b)
  
      dm = AudioClassificationData.from_files(
          train_files=[image_a, image_b],
          train_targets=[[0, 1, 0], [0, 1, 1]],
          val_files=[image_b, image_a],
          val_targets=[[1, 1, 0], [0, 0, 1]],
          test_files=[image_b, image_b],
          test_targets=[[0, 0, 1], [1, 1, 0]],
          batch_size=2,
          transform_kwargs={"spectrogram_size": (64, 64)},
      )
      # disable visualisation for testing
      assert dm.data_fetcher.block_viz_window is True
      dm.set_block_viz_window(False)
      assert dm.data_fetcher.block_viz_window is False
  
      # call show functions
      dm.show_train_batch()
      dm.show_train_batch("per_sample_transform")
      dm.show_val_batch("per_batch_transform")
  
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 9 for calling a global function with example usage: test_from_filepaths_visualise_multilabel(tmpdir) without return types