docstr: Takes a file_name to the input data and a function fn to extract the
  data. It compares the extracted data to the expected_data. The expected_dtype
  will be used to check that the extracted data is of the right type.
code: def _test_helper(self, file_name, expected_data, fn, expected_dtype):
      """Takes a file_name to the input data and a function fn to extract the
      data. It compares the extracted data to the expected_data. The expected_dtype
      will be used to check that the extracted data is of the right type.
      """
      test_filepath = common_utils.get_asset_path(file_name)
      expected_output = {
          "key" + str(idx + 1): torch.tensor(val, dtype=expected_dtype)
          for idx, val in enumerate(expected_data)
      }
  
      for key, vec in fn(test_filepath):
          self.assertTrue(key in expected_output)
          self.assertTrue(isinstance(vec, torch.Tensor))
          self.assertEqual(vec.dtype, expected_dtype)
          self.assertTrue(
              torch.all(torch.eq(vec, expected_output[key]))
          )
  
file_path: audio/test/torchaudio_unittest/kaldi_io_test.py
imports: import torch
  import torchaudio.kaldi_io
  from torchaudio_unittest import common_utils
label_desc: Use image node_id 1 for calling the Test_KaldiIO obj's underlying member method code with example usage: obj._test_helper(file_name, expected_data, fn, expected_dtype) without return types