code: def random_seed_20(args, dataset):
      """run for 20 random seeds"""
      alist = random.sample(range(1, 1000), 20)
      test_acc_fold = []
      for seed in alist:
          log.info("############ Seed %d ############" % seed)
          args.seed = seed
  
          test_acc_fold_, _ = split_10_cv(dataset, args)
          log.info(
              "Mean test acc at seed %d: %f" % (seed, test_acc_fold_)
          )
          test_acc_fold.append(test_acc_fold_)
  
      mean_acc = sum(test_acc_fold) / len(test_acc_fold)
      temp = [
          (acc - mean_acc) * (acc - mean_acc) for acc in test_acc_fold
      ]
      standard_std = math.sqrt(sum(temp) / len(test_acc_fold))
  
      log.info(
          "Final mean test acc using 20 random seeds(mean for 10-fold): %f"
          % (mean_acc)
      )
      log.info(
          "Final standard std using 20 random seeds(mean for 10-fold): %f"
          % (standard_std)
      )
  
imports: import sys
  import os
  import argparse
  import pgl
  from pgl.utils.logger import log
  import paddle
  import re
  import time
  import random
  import numpy
  import math
  import paddle
  import paddle.fluid
  import paddle.fluid.layers
  import pgl
  from pgl.utils.logger import log
  from model import GlobalModel
  from base_dataset import Subset, Dataset
  from dataloader import GraphDataloader
  from args import parser
  import warnings
  from sklearn.model_selection import KFold
label_desc: Use image node_id 3 for calling a global function with example usage: random_seed_20(args, dataset) without return types