code: def split_10_cv(dataset, args):
      """10 folds cross validation"""
      dataset.shuffle()
      X = np.array([0] * len(dataset))
      y = X
      kf = KFold(n_splits=10, shuffle=False)
  
      i = 1
      test_acc = []
      for train_index, test_index in kf.split(X, y):
          train_val_dataset = Subset(dataset, train_index)
          test_dataset = Subset(dataset, test_index)
          train_val_index_range = list(range(0, len(train_val_dataset)))
          num_val = int(len(train_val_dataset) / 9)
          val_dataset = Subset(
              train_val_dataset, train_val_index_range[:num_val]
          )
          train_dataset = Subset(
              train_val_dataset, train_val_index_range[num_val:]
          )
  
          log.info(
              "######%d fold of 10-fold cross validation######" % i
          )
          i += 1
          test_acc_ = main(
              args, train_dataset, val_dataset, test_dataset
          )
          test_acc.append(test_acc_)
  
      mean_acc = sum(test_acc) / len(test_acc)
      return mean_acc, test_acc
  
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 2 for calling a global function with example usage: split_10_cv(dataset, args) and returns: mean_acc,  test_acc