input: Write a function to classify the sentiment of movie reviews using Natural Language Processing. The function should take a sentence as input and return 'The sentiment of the sentence is negative!' if the negative score is higher than the positive score, otherwise it should return 'The sentiment of the sentence is positive!'. Use the vader module from nltk for sentiment analysis.
  assert classify_sentiment('The movie was terrible. It felt like it would never end!')=='The sentiment of the sentence is negative!'
  assert classify_sentiment('This was the best movie I have ever seen!')=='The sentiment of the sentence is positive!'
  assert classify_sentiment('The acting was okay but the plot was uninteresting.')=='The sentiment of the sentence is negative!'
  assert classify_sentiment('I did not like the movie at all.')=='The sentiment of the sentence is negative!'
  assert classify_sentiment('The movie was absolutely fantastic!')=='The sentiment of the sentence is positive!'
output: from nltk.sentiment.vader import SentimentIntensityAnalyzer
  
  def classify_sentiment(sentence):
      analyzer = SentimentIntensityAnalyzer()
      scores = analyzer.polarity_scores(sentence)
      if scores['neg'] > scores['pos']:
          return 'The sentiment of the sentence is negative!'
      else:
          return 'The sentiment of the sentence is positive!'