code: def random_split(dataset, split_ratio=0.7, seed=0, shuffle=True):
      """random splitter"""
      np.random.seed(seed)
      indices = list(range(len(dataset)))
      np.random.shuffle(indices)
      split = int(split_ratio * len(dataset))
      train_idx, valid_idx = indices[:split], indices[split:]
  
      log.info(
          "train_set : test_set == %d : %d"
          % (len(train_idx), len(valid_idx))
      )
      return Subset(dataset, train_idx), Subset(dataset, valid_idx)
  
imports: import os
  import sys
  import numpy
  from sklearn.model_selection import StratifiedKFold
  import pgl
  from pgl.utils.logger import log
label_desc: Use image node_id 2 for calling a global function with example usage: random_split(dataset, split_ratio, seed, shuffle) and returns: Subset,  Subset