code: def pack_units(self, input: torch.Tensor) -> torch.Tensor:
      if self.n_frames_per_step <= 1:
          return input
  
      offset = 4
      vocab_size = (
          len(self.tgt_dict) - offset
      )  # remove offset from <bos>, <pad>, <eos>, <unk>, which is specific to fairseq dictionary
  
      assert input.dim() == 1
      stacked_input = (
          input[:-1].view(-1, self.n_frames_per_step) - offset
      )  # remove <eos>
      scale = [
          pow(vocab_size, self.n_frames_per_step - 1 - i)
          for i in range(self.n_frames_per_step)
      ]
      scale = torch.LongTensor(scale).squeeze(0)
      res = input.new(
          (len(input) - 1) // self.n_frames_per_step + 1
      ).fill_(input[-1])
      res[:-1] = (stacked_input * scale).sum(dim=1) + offset
  
      return res
  
imports: import logging
  from dataclasses import dataclass
  from pathlib import Path
  from typing import Dict, List, Optional, Tuple
  import torch
  from fairseq.data import ConcatDataset, Dictionary
  from fairseq.data import data_utils
  from fairseq.data.audio.audio_utils import get_features_or_waveform
  from fairseq.data.audio.data_cfg import S2SDataConfig
  from fairseq.data.audio.speech_to_text_dataset import SpeechToTextDataset, SpeechToTextDatasetCreator, TextTargetMultitaskData, _collate_frames
label_desc: Use image node_id 2 for calling the SpeechToSpeechDataset obj's underlying member method code with example usage: obj.pack_units(input) and returns: res, input