new_contents: import os
  import random
  import string
  import tarfile
  from collections import defaultdict
  from unittest.mock import patch
  
  from parameterized import parameterized
  from torchtext.datasets.yahooanswers import YahooAnswers
  
  from ..common.case_utils import TempDirMixin, zip_equal
  from ..common.torchtext_test_case import TorchtextTestCase
  
  
  def _get_mock_dataset(root_dir):
      """
      root_dir: directory to the mocked dataset
      """
      base_dir = os.path.join(root_dir, "YahooAnswers")
      temp_dataset_dir = os.path.join(base_dir, "temp_dataset_dir")
      os.makedirs(temp_dataset_dir, exist_ok=True)
  
      seed = 1
      mocked_data = defaultdict(list)
      for file_name in ("train.csv", "test.csv"):
          txt_file = os.path.join(temp_dataset_dir, file_name)
          with open(txt_file, "w") as f:
              for i in range(5):
                  label = seed % 10 + 1
                  rand_string = " ".join(
                      random.choice(string.ascii_letters) for i in range(seed)
                  )
                  dataset_line = (label, f"{rand_string} {rand_string} {rand_string}")
                  # append line to correct dataset split
                  mocked_data[os.path.splitext(file_name)[0]].append(dataset_line)
                  f.write(f'"{label}","{rand_string}","{rand_string}","{rand_string}"\n')
                  seed += 1
  
      compressed_dataset_path = os.path.join(base_dir, "yahoo_answers_csv.tar.gz")
      # create tar file from dataset folder
      with tarfile.open(compressed_dataset_path, "w:gz") as tar:
          tar.add(temp_dataset_dir, arcname="yahoo_answers_csv")
  
      return mocked_data
  
  
  class TestYahooAnswers(TempDirMixin, TorchtextTestCase):
      root_dir = None
      samples = []
  
      @classmethod
      def setUpClass(cls):
          super().setUpClass()
          cls.root_dir = cls.get_base_temp_dir()
          cls.samples = _get_mock_dataset(cls.root_dir)
          cls.patcher = patch(
              "torchdata.datapipes.iter.util.cacheholder._hash_check", return_value=True
          )
          cls.patcher.start()
  
      @classmethod
      def tearDownClass(cls):
          cls.patcher.stop()
          super().tearDownClass()
  
      @parameterized.expand(["train", "test"])
      def test_yahoo_answers(self, split):
          dataset = YahooAnswers(root=self.root_dir, split=split)
  
          samples = list(dataset)
          expected_samples = self.samples[split]
          for sample, expected_sample in zip_equal(samples, expected_samples):
              self.assertEqual(sample, expected_sample)
  
      @parameterized.expand(["train", "test"])
      def test_yahoo_answers_split_argument(self, split):
          dataset1 = YahooAnswers(root=self.root_dir, split=split)
          (dataset2,) = YahooAnswers(root=self.root_dir, split=(split,))
  
          for d1, d2 in zip_equal(dataset1, dataset2):
              self.assertEqual(d1, d2)
  
subject: Add YahooAnswers Mocked Unit Test #1574 (#1577)