input: Write a python function to classify new documents using a trained Naive Bayes model.
  assert classify_documents(['God is love', 'OpenGL on the GPU is fast']) == clf.predict(X_new_tfidf)
  assert isinstance(classify_documents(['God is love', 'OpenGL on the GPU is fast']), np.ndarray)
  assert len(classify_documents(['God is love', 'OpenGL on the GPU is fast'])) == 2
output: def classify_documents(docs):
      from sklearn.feature_extraction.text import TfidfVectorizer
      from sklearn.naive_bayes import MultinomialNB
      import numpy as np
  
      # Assuming clf is a pre-trained Naive Bayes classifier and vectorizer is a pre-fitted TfidfVectorizer
      vectorizer = TfidfVectorizer()
      clf = MultinomialNB()
      
      # This should be replaced with loading the actual pre-trained model and vectorizer
      # Example: clf = joblib.load('naive_bayes_model.pkl')
      # Example: vectorizer = joblib.load('tfidf_vectorizer.pkl')
  
      X_new_tfidf = vectorizer.transform(docs)
      return clf.predict(X_new_tfidf)