code: def parse_args():
      parser = argparse.ArgumentParser(
          description="classification example. "
          "We fine-tune the pretrained model on GLUE dataset to do different taks."
      )
      parser.add_argument(
          "--model_name",
          type=str,
          default="google_en_uncased_bert_base",
          help="Name of the pretrained model.",
      )
      parser.add_argument(
          "--task_name",
          type=str,
          default="STS",
          help="Name of classification taks",
      )
      parser.add_argument(
          "--lr",
          type=float,
          default=5e-4,
          help="Initial learning rate. default is 2e-5",
      )
      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.",
      )
      parser.add_argument(
          "--epochs",
          type=int,
          default=3,
          help="Number of epochs, default is 3",
      )
      parser.add_argument(
          "--do_train", action="store_true", help="do training."
      )
      parser.add_argument(
          "--do_eval", action="store_true", help="do eval."
      )
      parser.add_argument(
          "--param_checkpoint",
          type=str,
          default=None,
          help="The parameter checkpoint for evaluating the model",
      )
      parser.add_argument(
          "--backbone_path",
          type=str,
          default=None,
          help="The parameter checkpoint of backbone model",
      )
      parser.add_argument(
          "--overwrite_cache",
          action="store_true",
          help="Whether to overwrite the feature cache.",
      )
      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(
          "--output_dir",
          type=str,
          default="cls_dir",
          help="The output directory where the model params will be written."
          " default is cls_dir",
      )
      parser.add_argument(
          "--log_interval",
          type=int,
          default=-1,
          help="The logging interval for training",
      )
      parser.add_argument(
          "--optimizer",
          type=str,
          default="adamw",
          help="The optimization algorithm",
      )
      parser.add_argument(
          "--batch_size",
          type=int,
          default=32,
          help="Batch size. Number of examples per gpu in a minibatch. default is 64",
      )
      parser.add_argument(
          "--seed", type=int, default=2, help="Random seed"
      )
  
      parser.add_argument(
          "--wd", type=float, default=0.01, help="weight decay"
      )
      parser.add_argument(
          "--max_grad_norm",
          type=float,
          default=1.0,
          help="Max gradient norm.",
      )
      parser.add_argument(
          "--train_dir",
          type=str,
          default=None,
          help="the path to training dataset",
      )
      parser.add_argument(
          "--eval_dir",
          type=str,
          default=None,
          help="the path to training dataset",
      )
      parser.add_argument(
          "--warmup_ratio",
          type=float,
          default=0.1,
          help="Ratio of warmup steps in the learning rate scheduler.",
      )
  
      args = parser.parse_args()
      return args
  
imports: import gluonnlp
  from tensorboardX import SummaryWriter
  import numpy
  import mxnet
  import json
  import random
  import pandas
  import os
  import logging
  import time
  import argparse
  import copy
  from mxnet.gluon.metric import Accuracy, F1, MCC, PearsonCorrelation, CompositeEvalMetric
  from classification_utils import get_task
  import matplotlib.pyplot
  from tqdm import tqdm
  from mxnet import gluon
  from gluonnlp.data.sampler import SplitSampler
  from mxnet.gluon import nn
  from gluonnlp.models import get_backbone
  from gluonnlp.utils.parameter import clip_grad_global_norm, count_parameters, deduplicate_param_dict
  from gluonnlp.utils.preprocessing import get_trimmed_lengths
  from gluonnlp.utils.misc import get_mxnet_visible_ctx, grouper, repeat, logging_config
  from mxnet.gluon.data import batchify
  from mxnet.gluon.data import DataLoader
  from mxnet.lr_scheduler import PolyScheduler
  from gluonnlp.utils import set_seed
  from gluonnlp.utils.misc import init_comm, parse_ctx
  from classification import TextPredictionNet
label_desc: Use image node_id 1 for calling a global function with example usage: parse_args() and returns: args