code: def __getitem__(self, index):
      import soundfile as sf
  
      fn = self.fnames[index]
      fn = fn if isinstance(self.fnames, list) else fn.as_py()
      fn = self.text_compressor.decompress(fn)
      path_or_fp = os.path.join(self.root_dir, fn)
      _path, slice_ptr = parse_path(path_or_fp)
      if len(slice_ptr) == 2:
          byte_data = read_from_stored_zip(
              _path, slice_ptr[0], slice_ptr[1]
          )
          assert is_sf_audio_data(byte_data)
          path_or_fp = io.BytesIO(byte_data)
  
      retry = 3
      wav = None
      for i in range(retry):
          try:
              wav, curr_sample_rate = sf.read(
                  path_or_fp, dtype="float32"
              )
              break
          except Exception as e:
              logger.warning(
                  f"Failed to read {path_or_fp}: {e}. Sleeping for {1 * i}"
              )
              time.sleep(1 * i)
  
      if wav is None:
          raise Exception(f"Failed to load {path_or_fp}")
  
      feats = torch.from_numpy(wav).float()
      feats = self.postprocess(feats, curr_sample_rate)
  
      v = {"id": index, "source": feats}
  
      if self.is_compute_mask:
          T = self._get_mask_indices_dims(feats.size(-1))
          mask = compute_block_mask_1d(
              shape=(self.clone_batch, T),
              mask_prob=self.mask_prob,
              mask_length=self.mask_length,
              mask_prob_adjust=self.mask_prob_adjust,
              inverse_mask=self.inverse_mask,
              require_same_masks=True,
              expand_adjcent=self.expand_adjacent,
              mask_dropout=self.mask_dropout,
              non_overlapping=self.non_overlapping,
          )
  
          v["precomputed_mask"] = mask
  
      return v
  
imports: import logging
  import os
  import sys
  import time
  import io
  import numpy
  import torch
  import torch.nn.functional
  from ..None import FairseqDataset
  from ..data_utils import compute_block_mask_1d, get_buckets, get_bucketed_sizes
  from fairseq.data.audio.audio_utils import parse_path, read_from_stored_zip, is_sf_audio_data
  from fairseq.data.text_compressor import TextCompressor, TextCompressionLevel
label_desc: Use image node_id 2 for calling the FileAudioDataset obj's underlying member method code with example usage: obj.__getitem__(index) and returns: v