code: def linear_regression():
      print("LinearRegression")
      X = np.array(
          [[1.0, 1.0], [1.0, 2.0], [2.0, 2.0], [2.0, 3.0]],
          dtype=np.float32,
      )
      # y = 1 * x_0 + 2 * x_1 + 3
      y = np.dot(X, np.array([1, 2], dtype=np.float32)) + 3
      reg = LinearRegression().fit(X, y)
      print("reg.score(X, y)")
      print(reg.score(X, y))
      print("reg.coef_")
      print(reg.coef_)
      print("reg.intercept_")
      print(reg.intercept_)
      print("reg.predict(np.array([[3, 5]], dtype=np.float32))")
      print(reg.predict(np.array([[3, 5]], dtype=np.float32)))
  
file_path: scikit-learn-intelex/tests/daal4py/sycl/sklearn_sycl.py
imports: import numpy
  from sklearn.cluster import DBSCAN, KMeans
  from sklearn.datasets import load_iris
  from sklearn.linear_model import LinearRegression, LogisticRegression
label_desc: Use image node_id 3 for calling a global function with example usage: linear_regression() without return types