code: def main():
      args = parse_args()
  
      level = logging.DEBUG if args.verbose else logging.INFO
      nlp.utils.misc.logging_config(
          args.ckpt_dir,
          name="pretrain_bert_" + str(args.local_rank),
          level=level,
          console=(args.local_rank in (0, -1)),
      )
      # Setup CUDA, GPU & distributed training
      local_size = 1
      if th.cuda.is_available() and args.cuda:
          th.cuda.set_device(
              args.local_rank if args.local_rank != -1 else 0
          )
          args.device = th.device(
              "cuda", args.local_rank if args.local_rank != -1 else 0
          )
          if args.local_rank != -1:
              distributed.init_process_group(backend="nccl")
              local_size = th.cuda.device_count()
      else:
          args.device = th.device("cpu")
          if args.local_rank != -1:
              distributed.init_process_group(backend="gloo")
  
      logging.info(args)
      logging.debug("Random seed set to {}".format(args.seed))
      set_seed(args.seed)
      logging.info(
          f"Training info: num_workers: {get_world_size(args)}, "
          f"local rank: {args.local_rank}"
      )
  
      train_tbl_id = np.random.bytes(20).hex()
      if args.local_rank not in (-1, 0):
          distributed.barrier()  # Wait for dataset
          train_tbl = nlp.utils.shm.load(
              args.mmap_folder / train_tbl_id
          )
      else:  # Main process
          if (
              args.local_rank != -1
              and (
                  args.mmap_folder / train_tbl_id / "meta.pkl"
              ).exists()
          ):
              distributed.barrier()  # Indicate dataset is ready
              train_tbl = nlp.utils.shm.load(
                  args.mmap_folder / train_tbl_id
              )
          else:
              (args.mmap_folder / train_tbl_id).mkdir(
                  exist_ok=True, parents=True
              )
              ds = pa.dataset.dataset(
                  args.input_files, format="feather"
              )
              # Without combining chunks tbl.take is 1000x slower
              train_tbl = ds.to_table().combine_chunks()
              if args.local_rank != -1:
                  nlp.utils.shm.serialize(
                      args.mmap_folder / train_tbl_id, train_tbl
                  )
                  distributed.barrier()  # Indicate dataset is ready
                  del train_tbl
                  train_tbl = nlp.utils.shm.load(
                      args.mmap_folder / train_tbl_id
                  )
  
      step_size = (
          args.batch_size * args.num_accumulated * get_world_size(args)
      )
      logging.info(f"Dataset has {len(train_tbl)} rows.")
      logging.info(
          f"Sampling {step_size} rows per step ({step_size/len(train_tbl)*100:.2f}% data)"
      )
      logging.info(
          f"Will iterate over the dataset during {args.num_steps} training steps "
          f"{args.num_steps * step_size/len(train_tbl):.2f} times."
      )
  
      train(args, tbl=train_tbl)
  
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 10 for calling a global function with example usage: main() without return types