code: def main():
      hfparser = HfArgumentParser(
          (
              ModelArguments,
              DataArguments,
              TrainingArguments,
              GenerationArguments,
          )
      )
      (
          model_args,
          data_args,
          training_args,
          generation_args,
          extra_args,
      ) = hfparser.parse_args_into_dataclasses(
          return_remaining_strings=True
      )
      training_args.generation_config = GenerationConfig(
          **vars(generation_args)
      )
  
      model_args.model_name = get_model_name(model_args.model_name)
      if training_args.lora_target_modules:
          training_args.lora_target_modules = eval(
              training_args.lora_target_modules
          )
      else:
          training_args.lora_target_modules = LORA_TARGET_MODULES_DICT[
              model_args.model_name
          ]
      args = argparse.Namespace(
          **vars(model_args), **vars(data_args), **vars(training_args)
      )
  
      seed_torch(training_args.seed)
      os.makedirs(training_args.output_dir, exist_ok=True)
      logging.basicConfig(
          format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
          datefmt="%m/%d/%Y %H:%M:%S",
          handlers=[
              logging.StreamHandler(sys.stdout),
              logging.FileHandler(
                  os.path.join(training_args.output_dir, "log.txt"),
                  mode="w",
                  encoding="utf-8",
              ),
          ],
      )
      logger.setLevel(logging.INFO)
  
      train(model_args, data_args, training_args, args)
  
file_path: DeepKE/example/llm/InstructKGC/src/finetune.py
imports: import os
  import sys
  import logging
  import argparse
  from datasets import Dataset
  import torch
  from transformers import HfArgumentParser, DataCollatorForSeq2Seq, GenerationConfig, BitsAndBytesConfig
  from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training, set_peft_model_state_dict
  from peft.tuners.lora import LoraLayer
  from utils.general_utils import LORA_TARGET_MODULES_DICT, seed_torch, get_model_tokenizer, get_model_name, get_format_time
  from utils.prompter import Prompter
  from utils.args import DataArguments, ModelArguments, TrainingArguments, GenerationArguments
  from datamodule import COLL_FN_DICT, DataCollatorForCPMBEE
label_desc: Use image node_id 5 for calling a global function with example usage: main() without return types