code: def create_dataset(self, n_subfolders=5, n_samples_per_subfolder=20):
      n_tot = n_subfolders * n_samples_per_subfolder
      dataset = torchvision.datasets.FakeData(
          size=n_tot, image_size=(3, 32, 32)
      )
  
      tmp_dir = tempfile.mkdtemp()
  
      folder_names = [f"folder_{i}" for i in range(n_subfolders)]
      sample_names = [
          f"img_{i}.jpg" for i in range(n_samples_per_subfolder)
      ]
  
      for folder_idx in range(n_subfolders):
          for sample_idx in range(n_samples_per_subfolder):
              idx = (folder_idx * n_subfolders) + sample_idx
              data = dataset[idx]
  
              self.ensure_dir(
                  os.path.join(tmp_dir, folder_names[folder_idx])
              )
  
              data[0].save(
                  os.path.join(
                      tmp_dir,
                      folder_names[folder_idx],
                      sample_names[sample_idx],
                  )
              )
      return tmp_dir, folder_names, sample_names
  
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 3 for calling the TestLightlyDataset obj's underlying member method code with example usage: obj.create_dataset(n_subfolders, n_samples_per_subfolder) and returns: tmp_dir,  folder_names,  sample_names