code: def parse_args():
      parser = argparse.ArgumentParser(description=__doc__)
      parser.add_argument(
          "--model_name",
          type=str,
          default="google_electra_small",
          help="Name of the pretrained model.",
      )
      parser.add_argument(
          "--do_train",
          action="store_true",
          help="Whether to train the model",
      )
      parser.add_argument(
          "--do_eval",
          action="store_true",
          help="Whether to evaluate the model",
      )
      parser.add_argument(
          "--data",
          type=str,
          default=None,
          help="Path to pretraining corpus file. File name with wildcard such as"
          " dir/*.npz is accepted. Or file name with wildcard such as dir/*.txt if"
          " --from_raw_text is set.",
      )
      parser.add_argument(
          "--output_dir",
          type=str,
          default="electra_owt",
          help="The output directory where the model params will be written."
          " default is squad_out",
      )
      # Training hyperparameters
      parser.add_argument(
          "--seed", type=int, default=100, help="Random seed"
      )
      parser.add_argument(
          "--log_interval",
          type=int,
          default=100,
          help="The logging interval.",
      )
      parser.add_argument(
          "--save_interval",
          type=int,
          default=1000,
          help="the number of steps to save model parameters."
          "default is every epoch",
      )
      # Data Loading from npz, need to be same as pretraining example
      parser.add_argument(
          "--max_seq_length",
          type=int,
          default=128,
          help="The maximum total input sequence length after tokenization."
          "Sequences longer than this will be truncated, and sequences shorter "
          "than this will be padded. default is 128",
      )
      parser.add_argument(
          "--do_lower_case",
          dest="do_lower_case",
          action="store_true",
          help="Lower case input text. Default is True",
      )
      parser.add_argument(
          "--no_lower_case",
          dest="do_lower_case",
          action="store_false",
          help="Don't lower case input text.",
      )
      parser.add_argument(
          "--mask_prob",
          type=float,
          default=0.15,
          help="mask probability for generator input",
      )
      parser.add_argument(
          "--replace_prob",
          type=float,
          default=0.85,
          help="probability of replacing mask tokens with generator predicted tokens.",
      )
      parser.set_defaults(do_lower_case=True)
      parser.add_argument(
          "--num_dataset_workers",
          type=int,
          default=4,
          help="Number of workers to pre-process dataset.",
      )
      parser.add_argument(
          "--num_batch_workers",
          type=int,
          default=2,
          help="Number of workers to pre-process mini-batch.",
      )
      parser.add_argument(
          "--num_buckets",
          type=int,
          default=1,
          help="Number of buckets for variable length sequence sampling",
      )
      # Data pre-processing from raw text. the below flags are only valid if --from_raw_text is set
      parser.add_argument(
          "--from_raw_text",
          action="store_true",
          help="If set, both training and dev samples are generated on-the-fly "
          "from raw texts instead of pre-processed npz files. ",
      )
      parser.add_argument(
          "--short_seq_prob",
          type=float,
          default=0.05,
          help="The probability of sampling sequences "
          "shorter than the max_seq_length.",
      )
      parser.add_argument(
          "--cached_file_path",
          default=None,
          help="Directory for saving preprocessed features",
      )
      parser.add_argument(
          "--circle_length",
          type=int,
          default=2,
          help="Number of files to be read for a single GPU at the same time.",
      )
      parser.add_argument(
          "--repeat",
          type=int,
          default=8,
          help="Number of times that files are repeated in each shuffle.",
      )
      # Optimization
      parser.add_argument(
          "--num_train_steps",
          type=int,
          default=1000000,
          help="The number of training steps. Note that epochs will be ignored "
          "if training steps are set",
      )
      parser.add_argument(
          "--warmup_steps",
          type=int,
          default=10000,
          help="warmup steps. Note that either warmup_steps or warmup_ratio is set.",
      )
      parser.add_argument(
          "--warmup_ratio",
          type=float,
          default=0.1,
          help="Ratio of warmup steps in the learning rate scheduler.",
      )
      parser.add_argument(
          "--batch_size",
          type=int,
          default=8,
          help="Batch size. Number of examples per gpu in a minibatch. default is 8",
      )
      parser.add_argument(
          "--max_grad_norm",
          type=float,
          default=1.0,
          help="Max gradient norm.",
      )
      parser.add_argument(
          "--optimizer",
          type=str,
          default="adamw",
          help="optimization algorithm. default is adamw",
      )
      parser.add_argument(
          "--lr_decay_power",
          type=float,
          default=1.0,
          help="Decay power for layer-wise learning rate",
      )
      parser.add_argument(
          "--num_accumulated",
          type=int,
          default=1,
          help="The number of batches for gradients accumulation to "
          "simulate large batch size.",
      )
      parser.add_argument(
          "--lr",
          type=float,
          default=5e-4,
          help="Initial learning rate. default is 5e-4",
      )
      parser.add_argument(
          "--wd", type=float, default=0.01, help="weight decay"
      )
      parser.add_argument(
          "--start_step",
          type=int,
          default=0,
          help="Start optimization step from the checkpoint.",
      )
      # Modle Configuration
      parser.add_argument(
          "--disc_weight",
          type=float,
          default=50.0,
          help="loss wight for discriminator",
      )
      parser.add_argument(
          "--gen_weight",
          type=float,
          default=1.0,
          help="loss wight for generator",
      )
      parser.add_argument(
          "--hidden_dropout_prob",
          type=float,
          default=0.1,
          help="dropout of hidden layer",
      )
      parser.add_argument(
          "--attention_dropout_prob",
          type=float,
          default=0.1,
          help="dropout of attention layer",
      )
      parser.add_argument(
          "--generator_units_scale",
          type=float,
          default=None,
          help="The scale size of the generator units",
      )
      parser.add_argument(
          "--generator_layers_scale",
          type=float,
          default=None,
          help="The scale size of the generator layer",
      )
      # Communication
      parser.add_argument(
          "--comm_backend",
          type=str,
          default="device",
          choices=["horovod", "dist_sync_device", "device"],
          help="Communication backend.",
      )
      parser.add_argument(
          "--gpus",
          type=str,
          default="0",
          help="list of gpus to run, e.g. 0 or 0,2,5. -1 means using cpu.",
      )
      args = parser.parse_args()
      return args
  
imports: import os
  import time
  import shutil
  import logging
  import argparse
  import functools
  import collections
  import mxnet
  import numpy
  from mxnet.lr_scheduler import PolyScheduler
  from sklearn import metrics
  from pretraining_utils import ElectraMasker, get_pretrain_data_npz, get_pretrain_data_text, get_electra_pretraining_model
  from gluonnlp.utils.misc import repeat, grouper, set_seed, init_comm, logging_config, naming_convention
  from gluonnlp.models.electra import ElectraModel, ElectraForPretrain
  from gluonnlp.utils.parameter import clip_grad_global_norm
label_desc: Use image node_id 1 for calling a global function with example usage: parse_args() and returns: args