code: def get_parser():
      parser = argparse.ArgumentParser(description=__doc__)
      parser.add_argument(
          "-i",
          "--input",
          required=True,
          help="path to extraed openwebtext dataset",
      )
      parser.add_argument(
          "-o",
          "--output",
          default="preprocessed_owt",
          help="directory for preprocessed features",
      )
      parser.add_argument(
          "--num_process",
          type=int,
          default=os.cpu_count(),
          help="number of processes for multiprocessing",
      )
      parser.add_argument(
          "--max_seq_length",
          type=int,
          default=128,
          help="the maximum length of the pretraining sequence",
      )
      parser.add_argument(
          "--num_out_files",
          type=int,
          default=1000,
          help="Number of desired output files, where each is processed"
          " independently by a worker.",
      )
      parser.add_argument(
          "--model_name",
          type=str,
          default="google_electra_small",
          help="Name of the pretrained model.",
      )
      parser.add_argument(
          "--shuffle",
          action="store_true",
          help="Wether to shuffle the data order",
      )
      parser.add_argument(
          "--do_lower_case",
          dest="do_lower_case",
          action="store_true",
          help="Lower case input text.",
      )
      parser.add_argument(
          "--no_lower_case",
          dest="do_lower_case",
          action="store_false",
          help="Don't lower case input text.",
      )
      parser.add_argument(
          "--short_seq_prob",
          type=float,
          default=0.05,
          help="The probability of sampling sequences shorter than"
          " the max_seq_length.",
      )
      parser.set_defaults(do_lower_case=True)
      return parser
  
file_path: gluon-nlp/scripts/pretraining/data_preprocessing.py
imports: import os
  import time
  import math
  import random
  import argparse
  import multiprocessing
  import numpy
  from pretraining_utils import get_all_features
  from gluonnlp.models import get_backbone
label_desc: Use image node_id 1 for calling a global function with example usage: get_parser() and returns: parser