code: def split_fold10(labels, fold_idx=0):
      skf = StratifiedKFold(n_splits=10, shuffle=True, random_state=0)
      idx_list = []
      for idx in skf.split(np.zeros(len(labels)), labels):
          idx_list.append(idx)
      train_idx, valid_idx = idx_list[fold_idx]
      return train_idx, valid_idx
  
imports: import argparse
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from dgl.data import GINDataset
  from dgl.dataloading import GraphDataLoader
  from dgl.nn.pytorch.conv import GINConv
  from dgl.nn.pytorch.glob import SumPooling
  from sklearn.model_selection import StratifiedKFold
  from torch.utils.data.sampler import SubsetRandomSampler
label_desc: Use image node_id 1 for calling a global function with example usage: split_fold10(labels, fold_idx) and returns: train_idx,  valid_idx