docstr: `sox_io_backend.load` can load given format correctly.
  
  file encodings introduce delay and boundary effects so
  we create a reference wav file from the original file format
  
   x
   |
   |    1. Generate given format with Sox
   |
   v    2. Convert to wav with Sox
  given format ----------------------> wav
   |                                   |
   |    3. Load with torchaudio        | 4. Load with scipy
   |                                   |
   v                                   v
  tensor ----------> x <----------- tensor
                 5. Compare
  
  Underlying assumptions are;
  i. Conversion of given format to wav with Sox preserves data.
  ii. Loading wav file with scipy is correct.
  
  By combining i & ii, step 2. and 4. allows to load reference given format
  data without using torchaudio
code: def assert_format(
      self,
      format: str,
      sample_rate: float,
      num_channels: int,
      compression: float = None,
      bit_depth: int = None,
      duration: float = 1,
      normalize: bool = True,
      encoding: str = None,
      atol: float = 4e-05,
      rtol: float = 1.3e-06,
  ):
      """`sox_io_backend.load` can load given format correctly.
  
      file encodings introduce delay and boundary effects so
      we create a reference wav file from the original file format
  
       x
       |
       |    1. Generate given format with Sox
       |
       v    2. Convert to wav with Sox
      given format ----------------------> wav
       |                                   |
       |    3. Load with torchaudio        | 4. Load with scipy
       |                                   |
       v                                   v
      tensor ----------> x <----------- tensor
                     5. Compare
  
      Underlying assumptions are;
      i. Conversion of given format to wav with Sox preserves data.
      ii. Loading wav file with scipy is correct.
  
      By combining i & ii, step 2. and 4. allows to load reference given format
      data without using torchaudio
      """
  
      path = self.get_temp_path(f"1.original.{format}")
      ref_path = self.get_temp_path("2.reference.wav")
  
      # 1. Generate the given format with sox
      sox_utils.gen_audio_file(
          path,
          sample_rate,
          num_channels,
          encoding=encoding,
          compression=compression,
          bit_depth=bit_depth,
          duration=duration,
      )
      # 2. Convert to wav with sox
      wav_bit_depth = 32 if bit_depth == 24 else None  # for 24-bit wav
      sox_utils.convert_audio_file(
          path, ref_path, bit_depth=wav_bit_depth
      )
      # 3. Load the given format with torchaudio
      data, sr = self._load(path, normalize=normalize)
      # 4. Load wav with scipy
      data_ref = load_wav(ref_path, normalize=normalize)[0]
      # 5. Compare
      assert sr == sample_rate
      self.assertEqual(data, data_ref, atol=atol, rtol=rtol)
  
imports: import itertools
  from functools import partial
  import torch
  from parameterized import parameterized
  from torchaudio._backend.utils import get_load_func
  from torchaudio_unittest.common_utils import get_asset_path, get_wav_data, load_wav, nested_params, PytorchTestCase, save_wav, skipIfNoExec, skipIfNoSox, skipIfNoSoxDecoder, sox_utils, TempDirMixin
  from .common import name_func
label_desc: Use image node_id 1 for calling the LoadTestBase obj's underlying member method code with example usage: obj.assert_format(format, sample_rate, num_channels, compression, bit_depth, duration, normalize, encoding, atol, rtol) without return types