code: def test_load_simple_classification_vision_data(
      correct_images_folder,
  ):
      (
          train_vision_data,
          test_vision_data,
      ) = simple.classification_dataset_from_directory(
          root=str(correct_images_folder),
          object_type="VisionData",
          image_extension="PNG",
      )
  
      for vision_data in [train_vision_data, test_vision_data]:
          batches = list(vision_data)
          assert_that(len(batches) == 1)
  
          images = batches[0].get("images")
          labels = batches[0].get("labels")
          assert_that(len(images) == 1 and len(labels) == 1)
  
          assert_that(
              images[0],
              all_of(
                  instance_of(np.ndarray),
                  has_property("shape", equal_to((10, 10, 3))),
              ),
          )
          assert_that(
              vision_data.batch_loader.dataset.reverse_classes_map[
                  labels[0]
              ]
              == "class1"
          )
  
file_path: deepchecks/tests/vision/datasets/test_visiondata_from_dir.py
imports: import pathlib
  from shutil import rmtree
  import numpy
  import PIL.Image
  import pytest
  from hamcrest import all_of, assert_that, calling, equal_to, has_length, has_property, instance_of, raises
  from torch.utils.data import DataLoader
  from deepchecks.utils.strings import get_random_string
  from deepchecks.vision.vision_data import simple_classification_data
label_desc: Use image node_id 5 for calling a global function with example usage: test_load_simple_classification_vision_data(correct_images_folder) without return types