input: def construct_naive_bayes_classifier(X_train, y_train, X_test):
      """ Constructs a Naive Bayes classifier to classify emails as spam or not spam.
      The classifier is based on the Gaussian Naive Bayes model.
      >>> from sklearn.datasets import load_iris
      >>> from sklearn.model_selection import train_test_split
      >>> iris = load_iris()
      >>> X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.5, random_state=0)
      >>> construct_naive_bayes_classifier(X_train, y_train, X_test)
      array([1, 1, 1, 0, 2, 1, 2, 0, 0, 2, 1, 0, 2, 1, 1, 2, 2, 0, 0, 2, 2, 2,
             1, 1, 2, 0, 0, 0, 0, 1, 1, 2, 1, 0, 1, 2, 2, 1, 1, 1])
      """
      
      from sklearn.naive_bayes import GaussianNB
      
      # Create a Gaussian Classifier
      model = GaussianNB()
      
      # Train the model using the training sets 
      model.fit(X_train, y_train)
      
      # Predict Output 
      y_pred = model.predict(X_test)
      return y_pred
output:     from sklearn.naive_bayes import GaussianNB
      
      # Create a Gaussian Classifier
      model = GaussianNB()
      
      # Train the model using the training sets 
      model.fit(X_train, y_train)
      
      # Predict Output 
      y_pred = model.predict(X_test)
      return y_pred