code: def parse_args():
      parser = argparse.ArgumentParser(
          description="Question Answering example. "
          "We fine-tune the pretrained model on SQuAD dataset."
      )
      parser.add_argument(
          "--model_name",
          type=str,
          default="google_albert_base_v2",
          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_dir", type=str, default="squad")
      parser.add_argument(
          "--version",
          default="2.0",
          choices=["1.1", "2.0"],
          help="Version of the SQuAD dataset.",
      )
      parser.add_argument(
          "--output_dir",
          type=str,
          default="squad_out",
          help="The output directory where the model params will be written."
          " default is squad_out",
      )
      # 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.",
      )
      # Training hyperparameters
      parser.add_argument(
          "--seed", type=int, default=100, help="Random seed"
      )
      parser.add_argument(
          "--log_interval",
          type=int,
          default=50,
          help="The logging interval for training",
      )
      parser.add_argument(
          "--eval_log_interval",
          type=int,
          default=10,
          help="The logging interval for evaluation",
      )
      parser.add_argument(
          "--save_interval",
          type=int,
          default=None,
          help="the number of steps to save model parameters."
          "default is every epoch",
      )
      parser.add_argument(
          "--epochs",
          type=float,
          default=3.0,
          help="Number of epochs, default is 3",
      )
      parser.add_argument(
          "--num_train_steps",
          type=int,
          default=None,
          help="The number of training steps. Note that epochs will be ignored "
          "if training steps are set",
      )
      parser.add_argument(
          "--batch_size",
          type=int,
          default=8,
          help="Batch size. Number of examples per gpu in a minibatch. default is 32",
      )
      parser.add_argument(
          "--eval_batch_size",
          type=int,
          default=16,
          help="Evaluate batch size. Number of examples per gpu in a minibatch for "
          "evaluation.",
      )
      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(
          "--adam_epsilon",
          type=float,
          default=1e-6,
          help="epsilon of AdamW optimizer",
      )
      parser.add_argument(
          "--adam_betas",
          default="(0.9, 0.999)",
          metavar="B",
          help="betas for Adam optimizer",
      )
      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=2e-5,
          help="Initial learning rate. default is 2e-5",
      )
      parser.add_argument(
          "--warmup_ratio",
          type=float,
          default=0.1,
          help="Ratio of warmup steps in the learning rate scheduler.",
      )
      parser.add_argument(
          "--warmup_steps",
          type=int,
          default=None,
          help="warmup steps. Note that either warmup_steps or warmup_ratio is set.",
      )
      parser.add_argument(
          "--wd", type=float, default=0.01, help="weight decay"
      )
      parser.add_argument(
          "--layerwise_decay",
          type=float,
          default=-1,
          help="Layer-wise lr decay",
      )
      parser.add_argument(
          "--untunable_depth",
          type=float,
          default=-1,
          help="Depth of untunable parameters",
      )
      parser.add_argument(
          "--classifier_dropout",
          type=float,
          default=0.1,
          help="dropout of classifier",
      )
      # Data pre/post processing
      parser.add_argument(
          "--max_seq_length",
          type=int,
          default=512,
          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 512",
      )
      parser.add_argument(
          "--doc_stride",
          type=int,
          default=128,
          help="When splitting up a long document into chunks, how much stride to "
          "take between chunks. default is 128",
      )
      parser.add_argument(
          "--max_query_length",
          type=int,
          default=64,
          help="The maximum number of tokens for the query. Questions longer than "
          "this will be truncated to this length. default is 64",
      )
      parser.add_argument(
          "--pre_shuffle_seed",
          type=int,
          default=100,
          help="Random seed for pre split shuffle",
      )
      parser.add_argument(
          "--round_to",
          type=int,
          default=8,
          help="The length of padded sequences will be rounded up to be multiple"
          " of this argument. When round to is set to 8, training throughput "
          "may increase for mixed precision training on GPUs with TensorCores.",
      )
      parser.add_argument(
          "--overwrite_cache",
          action="store_true",
          help="Whether to overwrite the feature cache.",
      )
      # Evaluation hyperparameters
      parser.add_argument(
          "--start_top_n",
          type=int,
          default=5,
          help="Number of start-position candidates",
      )
      parser.add_argument(
          "--end_top_n",
          type=int,
          default=5,
          help="Number of end-position candidates corresponding "
          "to a start position",
      )
      parser.add_argument(
          "--n_best_size",
          type=int,
          default=20,
          help="Top N results written to file",
      )
      parser.add_argument(
          "--max_answer_length",
          type=int,
          default=30,
          help="The maximum length of an answer that can be generated. This is "
          "needed because the start and end predictions are not conditioned "
          "on one another. default is 30",
      )
      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(
          "--all_evaluate",
          action="store_true",
          help="Whether to evaluate all intermediate checkpoints "
          "instead of only last one",
      )
      parser.add_argument(
          "--max_saved_ckpt",
          type=int,
          default=5,
          help="The maximum number of saved checkpoints",
      )
      parser.add_argument(
          "--dtype",
          type=str,
          default="float32",
          help="Data type used for evaluation. Either float32, float16 or int8. When you "
          "use --dtype float16, amp will be turned on in the training phase and "
          "fp16 will be used in evaluation. For now int8 data type is supported on CPU only.",
      )
      args = parser.parse_args()
      return args
  
imports: import os
  import json
  import time
  import logging
  import argparse
  import ast
  import functools
  import collections
  import dataclasses
  from dataclasses import dataclass
  from multiprocessing import Pool, cpu_count
  import mxnet
  import numpy
  from mxnet.lr_scheduler import PolyScheduler
  import gluonnlp.data.batchify
  from models import ModelForQABasic, ModelForQAConditionalV1
  from eval_utils import squad_eval
  from squad_utils import SquadFeature, get_squad_examples, convert_squad_example_to_feature
  from gluonnlp.models import get_backbone
  from gluonnlp.utils.misc import repeat, grouper, set_seed, init_comm, logging_config, parse_ctx
  from gluonnlp.initializer import TruncNorm
  from gluonnlp.data.sampler import SplitSampler
  from gluonnlp.utils.parameter import grad_global_norm, clip_grad_global_norm, count_parameters, deduplicate_param_dict
label_desc: Use image node_id 1 for calling a global function with example usage: parse_args() and returns: args