input: def classify_emails_as_spam(X_train, y_train, X_test):
      """ Constructs a naive bayes classifier to classify emails as spam or not spam.
      The classifier uses a GaussianNB model, which is trained on the provided training
      data and then used to predict whether the emails in the test data are spam or not.
      
      Example:
      >>> X_train = [[0, 1], [1, 0]]
      >>> y_train = [0, 1]
      >>> X_test = [[1, 1]]
      >>> classify_emails_as_spam(X_train, y_train, X_test)
      [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