code: def test_image_datasets_from_csv():
      N = 10
  
      def gen_fake_data(dir_name):
          paths = [f"{i}.jpg" for i in range(N)]
          labels = [i for i in range(N)]
          df = pd.DataFrame({"image": paths, "label": labels})
          df.to_csv(os.path.join(dir_name, "normal.csv"), index=False)
          df1 = pd.DataFrame(
              {"some_image": paths, "some_label": labels}
          )
          df1.to_csv(
              os.path.join(dir_name, "abnormal.csv"), index=False
          )
  
      with tempfile.TemporaryDirectory() as tmpdirname:
          gen_fake_data(tmpdirname)
          normal_csv = os.path.join(tmpdirname, "normal.csv")
          abnormal_csv = os.path.join(tmpdirname, "abnormal.csv")
          d1 = ImageClassificationDataset.from_csv(
              normal_csv, root=tmpdirname
          )
          assert "image" in d1.columns
          assert "label" in d1.columns
          d1 = ImageClassificationDataset(normal_csv)
          d2 = ImageClassificationDataset(
              abnormal_csv,
              image_column="some_image",
              label_column="some_label",
          )
          d2 = ImageClassificationDataset.from_csv(
              abnormal_csv,
              image_column="some_image",
              label_column="some_label",
          )
  
imports: import os
  import tempfile
  import pandas
  from gluoncv.auto.data.dataset import ImageClassificationDataset
  from gluoncv.auto.data.dataset import ObjectDetectionDataset
label_desc: Use image node_id 1 for calling a global function with example usage: test_image_datasets_from_csv() without return types