code: def set_logging(opt):
      """
      Generate the folder in which everything is saved.
      If opt.savename is given, folder will take on said name.
      If not, a name based on the start time is provided.
      If the folder already exists, it will by iterated until it can be created without
      deleting existing data.
      The current opt.save_path will be extended to account for the new save_folder name.
  
      Args:
          opt: argparse.Namespace, contains all training-specific parameters.
      Returns:
          Nothing!
      """
      checkfolder = opt.save_path + "/" + str(opt.iter)
  
      # Create start-time-based name if opt.savename is not give.
      if opt.savename == "":
          date = datetime.datetime.now()
          checkfolder = opt.save_path + "/" + str(opt.iter)
  
      # If folder already exists, iterate over it until is doesn't.
      # counter     = 1
      # while os.path.exists(checkfolder):
      #     checkfolder = opt.save_path+'/'+opt.savename+'_'+str(counter)
      #     counter += 1
  
      # Create Folder
      if not os.path.exists(checkfolder):
          os.makedirs(checkfolder)
      opt.save_path = checkfolder
  
      # Store training parameters as text and pickle in said folder.
      with open(opt.save_path + "/Parameter_Info.txt", "w") as f:
          f.write(gimme_save_string(opt))
      pkl.dump(opt, open(opt.save_path + "/hypa.pkl", "wb"))
  
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 10 for calling a global function with example usage: set_logging(opt) without return types