code: def collate_function(
      batch: List[Tuple[List[int], List[int]]], pad_token_id: int
  ) -> Dict[str, torch.Tensor]:
      """Collect a list of masked token indices, and labels, and
      batch them, padding to max length in the batch.
      """
      max_length = max(len(token_ids) for token_ids, _ in batch)
      padded_token_ids = [
          token_ids
          + [
              pad_token_id
              for _ in range(0, max_length - len(token_ids))
          ]
          for token_ids, _ in batch
      ]
      padded_labels = [
          labels
          + [pad_token_id for _ in range(0, max_length - len(labels))]
          for _, labels in batch
      ]
      src_tokens = torch.LongTensor(padded_token_ids)
      tgt_tokens = torch.LongTensor(padded_labels)
      attention_mask = src_tokens.ne(pad_token_id).type_as(src_tokens)
      return {
          "src_tokens": src_tokens,
          "tgt_tokens": tgt_tokens,
          "attention_mask": attention_mask,
      }
  
imports: import os
  import datetime
  import json
  import pathlib
  import re
  import string
  from functools import partial
  from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, TypeVar, Union
  import random
  import datasets
  import fire
  import logging
  import loguru
  import numpy
  import pytz
  import sh
  import torch
  import torch.nn
  import deepspeed
  from torch.utils.data import DataLoader, Dataset
  from torch.utils.tensorboard import SummaryWriter
  from transformers import AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerFast
  from transformers.models.roberta import RobertaConfig, RobertaModel
  from transformers.models.roberta.modeling_roberta import RobertaLMHead, RobertaPreTrainedModel
  from deepspeed.accelerator import get_accelerator
label_desc: Use image node_id 3 for calling a global function with example usage: collate_function(batch, pad_token_id) and returns: dict