code: def load_data():
      """Load dataset, use boston dataset"""
      diabetes = load_diabetes()
      X_train, X_test, y_train, y_test = train_test_split(
          diabetes.data,
          diabetes.target,
          random_state=99,
          test_size=0.25,
      )
      # normalize data
      ss_X = StandardScaler()
      ss_y = StandardScaler()
  
      X_train = ss_X.fit_transform(X_train)
      X_test = ss_X.transform(X_test)
      y_train = ss_y.fit_transform(y_train[:, None])[:, 0]
      y_test = ss_y.transform(y_test[:, None])[:, 0]
  
      return X_train, X_test, y_train, y_test
  
file_path: nni/examples/trials/sklearn/regression/main.py
imports: import nni
  from sklearn.datasets import load_diabetes
  from sklearn.model_selection import train_test_split
  from sklearn import linear_model
  import logging
  import numpy
  from sklearn.metrics import r2_score
  from sklearn.preprocessing import StandardScaler
  from sklearn.linear_model import LinearRegression
  from sklearn.linear_model import Ridge
  from sklearn.linear_model import Lars
  from sklearn.linear_model import ARDRegression
label_desc: Use image node_id 1 for calling a global function with example usage: load_data() and returns: X_train,  X_test,  y_train,  y_test