code: def test_class_label_to_and_from_dict(
      class_label_arg, tmp_path_factory
  ):
      names = ["negative", "positive"]
      names_file = str(
          tmp_path_factory.mktemp("features") / "labels.txt"
      )
      with open(names_file, "w", encoding="utf-8") as f:
          f.write("\n".join(names))
      if class_label_arg == "names":
          class_label = ClassLabel(names=names)
      elif class_label_arg == "names_file":
          class_label = ClassLabel(names_file=names_file)
      generated_class_label = generate_from_dict(asdict(class_label))
      assert generated_class_label == class_label
  
imports: import datetime
  from unittest import TestCase
  from unittest.mock import patch
  import numpy
  import pandas
  import pyarrow
  import pytest
  from datasets import Array2D
  from datasets.arrow_dataset import Dataset
  from datasets.features import Audio, ClassLabel, Features, Image, Sequence, Value
  from datasets.features.features import _arrow_to_datasets_dtype, _cast_to_python_objects, cast_to_python_objects, encode_nested_example, generate_from_dict, string_to_arrow
  from datasets.features.translation import Translation, TranslationVariableLanguages
  from datasets.info import DatasetInfo
  from datasets.utils.py_utils import asdict
  from ..utils import require_jax, require_tf, require_torch
label_desc: Use image node_id 5 for calling a global function with example usage: test_class_label_to_and_from_dict(class_label_arg, tmp_path_factory) without return types