code: def set_args():
      parser = argparse.ArgumentParser()
      parser.add_argument(
          "--train_path", default="data/train.json", type=str, help=""
      )
      parser.add_argument(
          "--model_dir", default="/model", type=str, help=""
      )
      parser.add_argument(
          "--num_train_epochs", default=20, type=int, help=""
      )
      parser.add_argument(
          "--train_batch_size", default=2, type=int, help=""
      )
      parser.add_argument(
          "--gradient_accumulation_steps", default=1, type=int, help=""
      )
      parser.add_argument(
          "--output_dir", default="output_dir_pt/", type=str, help=""
      )
      parser.add_argument("--log_steps", type=int, default=10, help="")
      parser.add_argument("--max_len", type=int, default=768, help="")
      parser.add_argument(
          "--max_src_len", type=int, default=450, help=""
      )
      parser.add_argument(
          "--pre_seq_len", type=int, default=16, help=""
      )
      parser.add_argument(
          "--prefix_projection", type=bool, default=True, help=""
      )
      parser.add_argument("--local_rank", type=int, default=0, help="")
      parser.add_argument(
          "--prompt_text", type=str, default="", help=""
      )
      return parser.parse_args()
  
file_path: DeepKE/example/llm/InstructKGC/src/finetuning_pt.py
imports: import argparse
  import os
  from shutil import copy
  import json
  import torch
  from torch.nn.utils.rnn import pad_sequence
  from torch.utils.data import Dataset, RandomSampler, DataLoader
  import deepspeed
  from transformers import AutoConfig, AutoTokenizer, AutoModel
label_desc: Use image node_id 3 for calling a global function with example usage: set_args() and returns: parser