code: def add_argument():
      parser = argparse.ArgumentParser(description="enwik8")
  
      parser.add_argument(
          "--with_cuda",
          default=False,
          action="store_true",
          help="use CPU in case there's no GPU support",
      )
      parser.add_argument(
          "--use_ema",
          default=False,
          action="store_true",
          help="whether use exponential moving average",
      )
      parser.add_argument(
          "-b",
          "--batch_size",
          default=32,
          type=int,
          help="mini-batch size (default: 32)",
      )
      parser.add_argument(
          "-e",
          "--epochs",
          default=30,
          type=int,
          help="number of total epochs (default: 30)",
      )
      parser.add_argument(
          "--local_rank",
          type=int,
          default=-1,
          help="local rank passed from distributed launcher",
      )
  
      parser = deepspeed.add_config_arguments(parser)
      args = parser.parse_args()
      return args
  
file_path: reformer-pytorch/examples/enwik8_deepspeed/train.py
imports: import deepspeed
  from reformer_pytorch import ReformerLM
  from reformer_pytorch.generative_tools import TrainingWrapper
  import argparse
  import random
  import tqdm
  import gzip
  import numpy
  import torch
  import torch.optim
  from torch.nn import functional
  from torch.utils.data import DataLoader, Dataset
label_desc: Use image node_id 1 for calling a global function with example usage: add_argument() and returns: args