code: def collate_fn(indices, *, args, tbl):
      batch = tbl.take(indices).to_pydict()
      pad_fn = nlp.torch.data.batchify.Pad()
      input_id = pad_fn(batch["quickthought1"] + batch["quickthought2"])
      segment_id = th.zeros_like(input_id)
      valid_length = th.tensor(
          batch["validlength1"] + batch["validlength2"]
      )
      mlm_positions = batch["mlmpositions1"] + batch["mlmpositions2"]
      # Masked positions with respect to flattened contextual_embedding (batch_size * seq_length, units)
      seq_length = input_id.shape[1]
      mlm_positions = [
          np.array(pos) + seq_length * i
          for i, pos in enumerate(mlm_positions)
      ]
      mlm_positions = th.tensor(
          np.concatenate(mlm_positions).astype(np.int64)
      )
      mlm_labels = batch["mlmlabels1"] + batch["mlmlabels2"]
      mlm_labels = th.tensor(
          np.concatenate(mlm_labels).astype(np.int64)
      )
      return (
          input_id,
          segment_id,
          valid_length,
          mlm_positions,
          mlm_labels,
      )
  
imports: import argparse
  import functools
  import json
  import logging
  import os
  import pathlib
  import random
  import shutil
  import time
  import warnings
  from contextlib import suppress
  import gluonnlp
  import numpy
  import pyarrow
  import pyarrow.compute
  import pyarrow.dataset
  import torch
  from fairscale.nn.data_parallel import ShardedDataParallel
  from fairscale.optim.grad_scaler import ShardedGradScaler
  from fairscale.optim.oss import OSS
  from torch import distributed
  from torch.nn.parallel import DistributedDataParallel
  from torch.utils.data import DataLoader, RandomSampler
  from torch.utils.data.distributed import DistributedSampler
  from torch.utils.tensorboard import SummaryWriter
label_desc: Use image node_id 8 for calling a global function with example usage: collate_fn(indices) and returns: input_id,  segment_id,  valid_length,  mlm_positions,  mlm_labels