code: def test_from_filepaths_visualise(tmpdir):
      tmpdir = Path(tmpdir)
  
      _rand_image().save(tmpdir / "e_1.png")
  
      train_images = [
          str(tmpdir / "e_1.png"),
          str(tmpdir / "e_1.png"),
          str(tmpdir / "e_1.png"),
      ]
  
      dm = AudioClassificationData.from_files(
          train_files=train_images,
          train_targets=[0, 3, 6],
          val_files=train_images,
          val_targets=[1, 4, 7],
          test_files=train_images,
          test_targets=[2, 5, 8],
          batch_size=2,
          num_workers=0,
      )
  
      # 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_train_batch(
          ["per_sample_transform", "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 8 for calling a global function with example usage: test_from_filepaths_visualise(tmpdir) without return types