code: def parse_arge():
      """Parse the arguments."""
      parser = argparse.ArgumentParser()
      # add model id and dataset path argument
      parser.add_argument(
          "--model_id",
          type=str,
          default="naver-clova-ix/donut-base",
          help="Model id to use for training.",
      )
      parser.add_argument(
          "--special_tokens",
          type=str,
          default=None,
          help="JSON string of special tokens to add to tokenizer.",
      )
      parser.add_argument(
          "--dataset_path",
          type=str,
          default="lm_dataset",
          help="Path to dataset.",
      )
      # add training hyperparameters for epochs, batch size, learning rate, and seed
      parser.add_argument(
          "--epochs",
          type=int,
          default=3,
          help="Number of epochs to train for.",
      )
      parser.add_argument(
          "--per_device_train_batch_size",
          type=int,
          default=1,
          help="Batch size to use for training.",
      )
      parser.add_argument(
          "--lr",
          type=float,
          default=5e-5,
          help="Learning rate to use for training.",
      )
      parser.add_argument(
          "--seed",
          type=int,
          default=42,
          help="Seed to use for training.",
      )
      parser.add_argument(
          "--gradient_checkpointing",
          type=bool,
          default=False,
          help="Path to deepspeed config file.",
      )
      parser.add_argument(
          "--bf16",
          type=bool,
          default=True
          if torch.cuda.get_device_capability()[0] == 8
          else False,
          help="Whether to use bf16.",
      )
      args = parser.parse_known_args()
      return args
  
file_path: hf-notebooks/sagemaker/26_document_ai_donut/scripts/train.py
imports: import os
  import argparse
  from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed, default_data_collator
  from datasets import load_from_disk
  import torch
  from transformers import Seq2SeqTrainingArguments, Seq2SeqTrainer, DonutProcessor, VisionEncoderDecoderModel, VisionEncoderDecoderConfig
  import shutil
  import logging
  import sys
  import json
label_desc: Use image node_id 1 for calling a global function with example usage: parse_arge() and returns: args