code: def logistic_regression_newton():
      print("LogisticRegression solver='newton-cg'")
      X, y = load_iris(return_X_y=True)
      clf = LogisticRegression(random_state=0, solver="newton-cg").fit(
          X.astype("float32"), y.astype("float32")
      )
      print("clf.predict(X[:2, :])")
      print(clf.predict(X[:2, :]))
      print("clf.predict_proba(X[:2, :])")
      print(clf.predict_proba(X[:2, :]))
      print("clf.score(X, y)")
      print(clf.score(X, y))
  
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 5 for calling a global function with example usage: logistic_regression_newton() without return types