code: def load_data(
      train_path="./data/regression.train",
      test_path="./data/regression.test",
  ):
      """
      Load or create dataset
      """
      print("Load data...")
      df_train = pd.read_csv(train_path, header=None, sep="\t")
      df_test = pd.read_csv(test_path, header=None, sep="\t")
      num = len(df_train)
      split_num = int(0.9 * num)
  
      y_train = df_train[0].values
      y_test = df_test[0].values
      y_eval = y_train[split_num:]
      y_train = y_train[:split_num]
  
      X_train = df_train.drop(0, axis=1).values
      X_test = df_test.drop(0, axis=1).values
      X_eval = X_train[split_num:, :]
      X_train = X_train[:split_num, :]
  
      # create dataset for lightgbm
      lgb_train = lgb.Dataset(X_train, y_train)
      lgb_eval = lgb.Dataset(X_eval, y_eval, reference=lgb_train)
  
      return lgb_train, lgb_eval, X_test, y_test
  
file_path: nni/examples/trials/auto-gbdt/main.py
imports: import logging
  import lightgbm
  import pandas
  from sklearn.metrics import mean_squared_error
  import nni
label_desc: Use image node_id 2 for calling a global function with example usage: load_data(train_path, test_path) and returns: lgb_train,  lgb_eval,  X_test,  y_test