input: Write a function to classify the sentiment of movie reviews using Natural Language Processing. The function should use the VADER sentiment analyzer from the NLTK library. If the negative sentiment score is greater than the positive sentiment score, return "The sentiment of the sentence is negative!"; otherwise, return "The sentiment of the sentence is positive!". If the input sentence is empty or contains only whitespace, return "Unable to determine sentiment."
  
  def classify_sentiment(sentence):
      if not sentence.strip():
          return "Unable to determine sentiment."
      
      import nltk
      from nltk.sentiment import SentimentIntensityAnalyzer
      
      sid = SentimentIntensityAnalyzer()
      scores = sid.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!'
  
  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 movie was amazing! Best time I had in years!')=='The sentiment of the sentence is positive!'
  assert classify_sentiment('It was okay.')=='The sentiment of the sentence is positive!'
  assert classify_sentiment('')=='Unable to determine sentiment.'
  assert classify_sentiment('     ')=='Unable to determine sentiment.'
output: def classify_sentiment(sentence):
      if not sentence.strip():
          return "Unable to determine sentiment."
      
      import nltk
      from nltk.sentiment import SentimentIntensityAnalyzer
      
      sid = SentimentIntensityAnalyzer()
      scores = sid.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!'