code: def gimme_save_string(opt):
      """
      Taking the set of parameters and convert it to easy-to-read string, which can be stored later.
  
      Args:
          opt: argparse.Namespace, contains all training-specific parameters.
      Returns:
          string, returns string summary of parameters.
      """
      varx = vars(opt)
      base_str = ""
      for key in varx:
          base_str += str(key)
          if isinstance(varx[key], dict):
              for sub_key, sub_item in varx[key].items():
                  base_str += (
                      "\n\t" + str(sub_key) + ": " + str(sub_item)
                  )
          else:
              base_str += "\n\t" + str(varx[key])
          base_str += "\n\n"
      return base_str
  
imports: import warnings
  import csv
  import datetime
  import os
  import pickle
  import faiss
  import matplotlib.pyplot
  import numpy
  import torch
  from PIL import Image
  from sklearn import metrics
  from torch import nn
  from tqdm import tqdm
  import pdb
label_desc: Use image node_id 3 for calling a global function with example usage: gimme_save_string(opt) and returns: base_str