code: def __getitem__(self, index):
      code, dur, f0 = self._get_raw_item(index)
      code = torch.cat([code.new([self.dictionary.bos()]), code])
  
      # use 0 for eos and bos
      dur = torch.cat([dur.new([0]), dur])
      if self.discrete_dur:
          dur = self.dur_dictionary.encode_line(
              " ".join(map(str, dur.tolist())), append_eos=False
          ).long()
      else:
          dur = dur.float()
  
      # TODO: find a more elegant approach
      raw_f0 = None
      if self.discrete_f0:
          if self.config.f0_vq_type == "precomp":
              f0 = self.f0_dictionary.encode_line(
                  " ".join(map(str, f0.tolist())), append_eos=False
              ).long()
          else:
              f0 = f0.float()
              f0 = self.preprocess_f0(
                  f0, self.f0_stats[self.spkrs[index]]
              )
              if self.return_continuous_f0:
                  raw_f0 = f0
                  raw_f0 = torch.cat(
                      [raw_f0.new([self.f0_dictionary.bos()]), raw_f0]
                  )
              f0 = naive_quantize(f0, self._f0_quantizer)
          f0 = torch.cat([f0.new([self.f0_dictionary.bos()]), f0])
      else:
          f0 = f0.float()
          if self.multispkr:
              f0 = self.preprocess_f0(
                  f0, self.f0_stats[self.spkrs[index]]
              )
          else:
              f0 = self.preprocess_f0(f0, self.f0_stats)
          f0 = torch.cat([f0.new([0]), f0])
  
      if raw_f0 is not None:
          *_, raw_f0, raw_f0_mask = self.shifts(code, dur, raw_f0)
      else:
          raw_f0_mask = None
  
      code, code_mask, dur, dur_mask, f0, f0_mask = self.shifts(
          code, dur, f0
      )
      if raw_f0_mask is not None:
          assert (raw_f0_mask == f0_mask).all()
  
      # is a padded frame if either input or output is padded
      feats = {
          "source": code[:-1],
          "target": code[1:],
          "mask": code_mask[1:].logical_or(code_mask[:-1]),
          "dur_source": dur[:-1],
          "dur_target": dur[1:],
          "dur_mask": dur_mask[1:].logical_or(dur_mask[:-1]),
          "f0_source": f0[:-1],
          "f0_target": f0[1:],
          "f0_mask": f0_mask[1:].logical_or(f0_mask[:-1]),
      }
  
      if raw_f0 is not None:
          feats["raw_f0"] = raw_f0[1:]
  
      if self.return_filename:
          fname = self.file_names[index]
          feats["filename"] = (
              fname
              if not self.strip_filename
              else Path(fname).with_suffix("").name
          )
      return feats
  
imports: import json
  import logging
  import os
  import random
  from pathlib import Path
  import numpy
  import torch
  import torch.utils.data
  from .None import data_utils
  from fairseq.data.fairseq_dataset import FairseqDataset
label_desc: Use image node_id 5 for calling the CodeDataset obj's underlying member method code with example usage: obj.__getitem__(index) and returns: feats