code: def test_from_filepaths_list_image_paths(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"),
      ]
  
      spectrograms_data = 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,
      )
  
      # check training data
      data = next(iter(spectrograms_data.train_dataloader()))
      imgs, labels = data["input"], data["target"]
      assert imgs.shape == (2, 3, 128, 128)
      assert labels.shape == (2,)
      assert labels.numpy()[0] in [0, 3, 6]  # data comes shuffled here
      assert labels.numpy()[1] in [0, 3, 6]  # data comes shuffled here
  
      # check validation data
      data = next(iter(spectrograms_data.val_dataloader()))
      imgs, labels = data["input"], data["target"]
      assert imgs.shape == (2, 3, 128, 128)
      assert labels.shape == (2,)
      assert list(labels.numpy()) == [1, 4]
  
      # check test data
      data = next(iter(spectrograms_data.test_dataloader()))
      imgs, labels = data["input"], data["target"]
      assert imgs.shape == (2, 3, 128, 128)
      assert labels.shape == (2,)
      assert list(labels.numpy()) == [2, 5]
  
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 7 for calling a global function with example usage: test_from_filepaths_list_image_paths(tmpdir) without return types