code: def test_load_simple_classification_dataset_only_train(
      correct_images_folder,
  ):
      data_loader = simple.classification_dataset_from_directory(
          root=str(correct_images_folder.joinpath("train")),
          object_type="DataLoader",
          image_extension="PNG",
      )
  
      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 4 for calling a global function with example usage: test_load_simple_classification_dataset_only_train(correct_images_folder) without return types