input: 
  def sentiment_analyzer(text):
      """ Analyze the sentiment of a given text by tokenizing, stemming, removing punctuation and stopwords, 
      and then calculating the polarity score using VADER sentiment analysis.
      >>> sentiment_analyzer('I love this product!')
      {'neg': 0.0, 'neu': 0.217, 'pos': 0.783, 'compound': 0.7932}
      >>> sentiment_analyzer('This is the worst book I have ever read.')
      {'neg': 0.484, 'neu': 0.516, 'pos': 0.0, 'compound': -0.5267}
      """
  
output:     from nltk.tokenize import word_tokenize
      from nltk.stem import PorterStemmer
      from nltk.corpus import stopwords
      from nltk.sentiment.vader import SentimentIntensityAnalyzer
      import string
  
      # Tokenize the text
      tokens = word_tokenize(text.lower())
  
      # Remove punctuation
      tokens = [token for token in tokens if token not in string.punctuation]
  
      # Remove stopwords
      stop_words = set(stopwords.words('english'))
      tokens = [token for token in tokens if token not in stop_words]
  
      # Stem the tokens
      stemmer = PorterStemmer()
      tokens = [stemmer.stem(token) for token in tokens]
  
      # Join the tokens back into a single string
      processed_text = ' '.join(tokens)
  
      # Analyze sentiment using VADER
      analyzer = SentimentIntensityAnalyzer()
      sentiment_scores = analyzer.polarity_scores(processed_text)
  
      return sentiment_scores