code: def regression_data():
      from sklearn.linear_model import LinearRegression
  
      X, y = datasets.make_regression(
          n_samples=2000,
          n_features=4,
          n_informative=3,
          noise=30.0,
          random_state=0,
      )
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, random_state=0
      )
      model = LinearRegression()
      model.fit(X_train, y_train)
      y_pred = model.predict(X_test)
      return y_test, y_pred
  
imports: from copy import copy
  import sys
  import os
  from pathlib import Path
  import numpy
  from sklearn.linear_model import LogisticRegression
  import pytest
  from matplotlib.testing.conftest import mpl_test_settings, mpl_image_comparison_parameters
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.model_selection import GridSearchCV
  from sklearn import datasets
  from sklearn.model_selection import train_test_split
label_desc: Use image node_id 14 for calling a global function with example usage: regression_data() and returns: y_test,  y_pred