code: def test_gnb_naive_bayes_scale_invariance():
      # Scaling the data should not change the prediction results
      iris = load_iris()
      X, y = iris.data, iris.target
      labels = [
          GaussianNB().fit(f * X, y).predict(f * X)
          for f in [1e-10, 1, 1e10]
      ]
      assert_array_equal(labels[0], labels[1])
      assert_array_equal(labels[1], labels[2])
  
imports: import re
  import warnings
  import numpy
  import pytest
  from scipy.special import logsumexp
  from sklearn.datasets import load_digits, load_iris
  from sklearn.model_selection import cross_val_score, train_test_split
  from sklearn.naive_bayes import BernoulliNB, CategoricalNB, ComplementNB, GaussianNB, MultinomialNB
  from sklearn.utils._testing import assert_allclose, assert_almost_equal, assert_array_almost_equal, assert_array_equal
  from sklearn.utils.fixes import CSR_CONTAINERS
label_desc: Use image node_id 14 for calling a global function with example usage: test_gnb_naive_bayes_scale_invariance() without return types