input: Write a python function to build and train a Naive Bayes classifier using sklearn and make a prediction on a given document.
  assert naive_bayes_predict(["The sky is blue.", "Violets are red.", "I love programming.", "JavaScript is fun.", "Python is great!"], [0, 0, 1, 1, 1], "I love Python.") == 1
  assert naive_bayes_predict(["Cats are cute.", "Dogs are loyal.", "Birds can fly.", "Fish swim in the water.", "Lions are wild."], [0, 0, 1, 1, 0], "Dogs are loyal.") == 0
  assert naive_bayes_predict(["The car is fast.", "The bus is slow.", "Trains are reliable.", "Planes can fly.", "Ships sail the seas."], [1, 0, 1, 1, 0], "Planes can fly.") == 1
output: def naive_bayes_predict(documents, labels, test_document):
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
  
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(documents)
      clf = MultinomialNB()
      clf.fit(X, labels)
  
      test_X = vectorizer.transform([test_document])
      prediction = clf.predict(test_X)
  
      return prediction[0]