code: def test_load_simple_classification_dataset(correct_images_folder):
      train_test_dataloaders = (
          simple.classification_dataset_from_directory(
              root=str(correct_images_folder),
              object_type="DataLoader",
              image_extension="PNG",
          )
      )
  
      for data_loader in train_test_dataloaders:
          assert_that(
              data_loader,
              all_of(
                  instance_of(DataLoader),
                  has_length(1),
                  has_property(
                      "dataset",
                      instance_of(simple.SimpleClassificationDataset),
                  ),
              ),
          )
  
          img, label = data_loader.dataset[0]
  
          assert_that(
              img,
              all_of(
                  instance_of(np.ndarray),
                  has_property("shape", equal_to((10, 10, 3))),
              ),
          )
  
          assert_that(label, instance_of(int))
          assert_that(
              data_loader.dataset.reverse_classes_map[label] == "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 3 for calling a global function with example usage: test_load_simple_classification_dataset(correct_images_folder) without return types