code: def test_from_filepaths_numpy(tmpdir):
      tmpdir = Path(tmpdir)
  
      np.save(str(tmpdir / "a_1.npy"), np.random.rand(64, 64, 3))
      np.save(str(tmpdir / "b_1.npy"), np.random.rand(64, 64, 3))
  
      train_images = [
          str(tmpdir / "a_1.npy"),
          str(tmpdir / "b_1.npy"),
      ]
  
      spectrograms_data = AudioClassificationData.from_files(
          train_files=train_images,
          train_targets=[1, 2],
          batch_size=2,
          num_workers=0,
      )
      assert spectrograms_data.train_dataloader() is not None
  
      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 sorted(labels.numpy()) == [1, 2]
  
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 6 for calling a global function with example usage: test_from_filepaths_numpy(tmpdir) without return types