code: def arg_parse():
      parser = argparse.ArgumentParser(
          description="Quantize a model with LoftQ."
      )
      parser.add_argument(
          "--model_name_or_path",
          type=str,
          default=None,
          required=True,
          help="The name or path of the fp32/16 model.",
      )
      parser.add_argument(
          "--token",
          type=str,
          default=None,
          help="The access token to download model from HuggingFace Hub.",
      )
      parser.add_argument(
          "--bits",
          type=int,
          default=4,
          help="The quantized bits",
      )
      parser.add_argument(
          "--iter",
          type=int,
          default=1,
          help="The alternating steps in LoftQ",
      )
      parser.add_argument(
          "--rank",
          type=int,
          default=16,
          help="The rank of the LoRA adapter",
      )
      parser.add_argument(
          "--save_dir",
          type=str,
          default="./model_zoo/loftq/",
          help="The rank of the LoRA adapter",
      )
      args = parser.parse_args()
      return args
  
file_path: peft/examples/loftq_finetuning/quantize_save_load.py
imports: import argparse
  import os
  import torch
  import torch.nn
  from transformers import AutoModelForCausalLM, AutoModelForSeq2SeqLM, AutoModelForSequenceClassification, AutoTokenizer
  from peft import LoftQConfig, LoraConfig, TaskType, get_peft_model
label_desc: Use image node_id 3 for calling a global function with example usage: arg_parse() and returns: args