code: def get_nfold_split(ifold, nfold=10, meta_version=1):
      if meta_version == 2:
          return get_nfold_split2(ifold, nfold)
  
      meta = pd.read_csv(settings.META_FILE, na_filter=False)
      meta_train = meta[meta["is_train"] == 1]
  
      kf = KFold(n_splits=nfold)
      for i, (train_index, valid_index) in enumerate(
          kf.split(meta_train[settings.ID_COLUMN].values.reshape(-1))
      ):
          if i == ifold:
              break
      return meta_train.iloc[train_index], meta_train.iloc[valid_index]
  
imports: import os
  import json
  import sys
  import time
  import numpy
  import pandas
  from PIL import Image
  from tqdm import tqdm
  from pycocotools import mask
  from sklearn.model_selection import KFold
  import settings
label_desc: Use image node_id 12 for calling a global function with example usage: get_nfold_split(ifold, nfold, meta_version) and returns: meta_train,  meta_train, get_nfold_split2