code: def test_gnb():
      # Gaussian Naive Bayes classification.
      # This checks that GaussianNB implements fit and predict and returns
      # correct values for a simple toy dataset.
  
      clf = GaussianNB()
      y_pred = clf.fit(X, y).predict(X)
      assert_array_equal(y_pred, y)
  
      y_pred_proba = clf.predict_proba(X)
      y_pred_log_proba = clf.predict_log_proba(X)
      assert_array_almost_equal(
          np.log(y_pred_proba), y_pred_log_proba, 8
      )
  
      # Test whether label mismatch between target y and classes raises
      # an Error
      # FIXME Remove this test once the more general partial_fit tests are merged
      with pytest.raises(
          ValueError,
          match="The target label.* in y do not exist in the initial classes",
      ):
          GaussianNB().partial_fit(X, y, classes=[0, 1])
  
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 3 for calling a global function with example usage: test_gnb() without return types