code: def test_check_images(self):
      # create a dataset
      tmp_dir = tempfile.mkdtemp()
      n_healthy = 100
      n_corrupt = 20
  
      dataset = torchvision.datasets.FakeData(
          size=n_healthy, image_size=(3, 32, 32)
      )
      sample_names = [f"img_{i}.jpg" for i in range(n_healthy)]
      for sample_name, data in zip(sample_names, dataset):
          path = os.path.join(tmp_dir, sample_name)
          data[0].save(path)
  
      corrupt_sample_names = [
          f"img_{i}.jpg"
          for i in range(n_healthy, n_healthy + n_corrupt)
      ]
      for sample_name in corrupt_sample_names:
          path = os.path.join(tmp_dir, sample_name)
          with open(path, "a") as f:
              f.write("this_is_not_an_image")
  
      # tests
      healthy_images, corrupt_images = check_images(tmp_dir)
      assert len(healthy_images) == n_healthy
      assert len(corrupt_images) == n_corrupt
  
imports: import os
  import shutil
  import tempfile
  import unittest
  from typing import List, Tuple
  import numpy
  import torchvision
  from PIL.Image import Image
  from lightly.data import LightlyDataset
  from lightly.data._utils import check_images
label_desc: Use image node_id 7 for calling the TestLightlyDataset obj's underlying member method code with example usage: obj.test_check_images() without return types