code: def collater(self, samples):
      pad_idx, eos_idx = self.dictionary.pad(), self.dictionary.eos()
      if len(samples) == 0:
          return {}
  
      src_tokens = data_utils.collate_tokens(
          [s["source"] for s in samples],
          pad_idx,
          eos_idx,
          left_pad=False,
      )
  
      tgt_tokens = data_utils.collate_tokens(
          [s["target"] for s in samples],
          pad_idx=pad_idx,
          eos_idx=pad_idx,  # appending padding, eos is there already
          left_pad=False,
      )
  
      src_durs, tgt_durs = [
          data_utils.collate_tokens(
              [s[k] for s in samples],
              pad_idx=self.pads.dur,
              eos_idx=self.pads.dur,
              left_pad=False,
          )
          for k in ["dur_source", "dur_target"]
      ]
  
      src_f0s, tgt_f0s = [
          data_utils.collate_tokens(
              [s[k] for s in samples],
              pad_idx=self.pads.f0,
              eos_idx=self.pads.f0,
              left_pad=False,
          )
          for k in ["f0_source", "f0_target"]
      ]
  
      mask, dur_mask, f0_mask = [
          data_utils.collate_tokens(
              [s[k] for s in samples],
              pad_idx=1,
              eos_idx=1,
              left_pad=False,
          )
          for k in ["mask", "dur_mask", "f0_mask"]
      ]
  
      src_lengths = torch.LongTensor(
          [s["source"].numel() for s in samples]
      )
      n_tokens = sum(len(s["source"]) for s in samples)
  
      result = {
          "nsentences": len(samples),
          "ntokens": n_tokens,
          "net_input": {
              "src_tokens": src_tokens,
              "src_lengths": src_lengths,
              "dur_src": src_durs,
              "f0_src": src_f0s,
          },
          "target": tgt_tokens,
          "dur_target": tgt_durs,
          "f0_target": tgt_f0s,
          "mask": mask,
          "dur_mask": dur_mask,
          "f0_mask": f0_mask,
      }
  
      if "filename" in samples[0]:
          result["filename"] = [s["filename"] for s in samples]
  
      # TODO: remove this hack into the inference dataset
      if "prefix" in samples[0]:
          result["prefix"] = [s["prefix"] for s in samples]
  
      if "raw_f0" in samples[0]:
          raw_f0s = data_utils.collate_tokens(
              [s["raw_f0"] for s in samples],
              pad_idx=self.pads.f0,
              eos_idx=self.pads.f0,
              left_pad=False,
          )
          result["raw_f0"] = raw_f0s
      return result
  
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 9 for calling the CodeDataset obj's underlying member method code with example usage: obj.collater(samples) and returns: result, dict