input: Write a function to construct a naive bayes classifier using python to classify emails as spam or not spam. The function should load the training data, fit the model, and then predict the output for test data. Return the predicted output for the test data.
  from sklearn.naive_bayes import GaussianNB
  def construct_spam_classifier(X_train, y_train, X_test):
      # 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
  assert construct_spam_classifier([[1,2],[2,3],[3,4]], [0,1,1], [[1,1],[2,2]]) == [0,1]
  assert construct_spam_classifier([[1,0],[0,1]], [0,1], [[1,1],[0,0]]) == [1,0]
  assert construct_spam_classifier([[0,0],[1,1]], [1,0], [[0,0],[1,1]]) == [1,0]
output: 
  def construct_spam_classifier(X_train, y_train, X_test):
      model = GaussianNB()
      model.fit(X_train, y_train)
      y_pred = model.predict(X_test)
      return y_pred
  