code: def test():
      meta = pd.read_csv(settings.META_FILE)
      meta_train = meta[meta["is_train"] == 1]
      print(type(meta_train))
  
      cv = utils.KFoldBySortedValue()
      for train_idx, valid_idx in cv.split(
          meta_train[settings.DEPTH_COLUMN].values.reshape(-1)
      ):
          print(len(train_idx), len(valid_idx))
          print(train_idx[:10])
          print(valid_idx[:10])
          # break
  
      meta_train_split, meta_valid_split = (
          meta_train.iloc[train_idx],
          meta_train.iloc[valid_idx],
      )
      print(type(meta_train_split))
      print(meta_train_split[settings.X_COLUMN].values[:10])
  
file_path: nni/examples/trials/kaggle-tgs-salt/preprocess.py
imports: import os
  import pandas
  import numpy
  import json
  import torch
  import torch.nn
  from keras.preprocessing.image import load_img
  from sklearn.model_selection import StratifiedKFold
  import settings
  import utils
label_desc: Use image node_id 4 for calling a global function with example usage: test() without return types