code: def collater(
      self, samples: List[SpeechToTextJointDatasetItem]
  ) -> Dict:
      s2t_out = super().collater(samples, return_order=True)
      if s2t_out == {}:
          return s2t_out
      net_input, order = s2t_out["net_input"], s2t_out["order"]
  
      if self.src_texts is not None and self.src_dict is not None:
          src_txt_tokens = fairseq_data_utils.collate_tokens(
              [x.src_txt_tokens for x in samples],
              self.src_dict.pad(),
              self.src_dict.eos(),
              left_pad=False,
              move_eos_to_beginning=False,
          )
          src_txt_lengths = torch.tensor(
              [x.src_txt_tokens.size()[0] for x in samples],
              dtype=torch.long,
          )
          if self.use_src_lang_id > 0:
              src_lang_idxs = torch.tensor(
                  [s.src_lang_tag for s in samples],
                  dtype=src_txt_tokens.dtype,
              )
              if self.use_src_lang_id == 1:  # replace eos with lang_id
                  eos_idx = src_txt_lengths - 1
                  src_txt_tokens.scatter_(
                      1, eos_idx.view(-1, 1), src_lang_idxs.view(-1, 1)
                  )
              else:
                  raise NotImplementedError(
                      "Implementation is required"
                  )
  
          src_txt_tokens = src_txt_tokens.index_select(0, order)
          src_txt_lengths = src_txt_lengths.index_select(0, order)
          net_input["src_txt_tokens"] = src_txt_tokens
          net_input["src_txt_lengths"] = src_txt_lengths
  
      net_input["alignment"] = None
      if self.alignment is not None:
          max_len = max([s.tgt_alignment.size(0) for s in samples])
          alignment = torch.ones(len(samples), max_len).float()
          for i, s in enumerate(samples):
              cur_len = s.tgt_alignment.size(0)
              alignment[i][:cur_len].copy_(s.tgt_alignment)
          net_input["alignment"] = alignment.index_select(0, order)
  
      if (
          self.tgt_texts is not None
          and samples[0].tgt_lang_tag is not None
      ):
          for i in range(len(samples)):
              net_input["prev_output_tokens"][i][0] = samples[
                  order[i]
              ].tgt_lang_tag
  
      out = {
          "id": s2t_out["id"],
          "net_input": net_input,
          "target": s2t_out["target"],
          "target_lengths": s2t_out["target_lengths"],
          "ntokens": s2t_out["ntokens"],
          "nsentences": len(samples),
      }
      return out
  
imports: import logging
  from pathlib import Path
  from typing import Dict, List, NamedTuple, Optional
  import torch
  from fairseq.data import ConcatDataset, Dictionary, ResamplingDataset
  from fairseq.data import data_utils
  from fairseq.data.audio.speech_to_text_dataset import S2TDataConfig, SpeechToTextDataset, SpeechToTextDatasetCreator
label_desc: Use image node_id 5 for calling the SpeechToTextJointDataset obj's underlying member method code with example usage: obj.collater(samples) and returns: out, s2t_out