input: 
  def sentiment_analyzer(text):
      """This function analyzes the sentiment of a given text by tokenizing it, stemming the tokens,
      removing punctuation and stopwords, and then using the SentimentIntensityAnalyzer to determine
      the polarity score of the text. The function returns a dictionary containing the polarity score.
  
      >>> sentiment_analyzer("I love this product!")
      {'neg': 0.0, 'neu': 0.25, 'pos': 0.75, 'compound': 0.875}
      >>> sentiment_analyzer("This is a very bad experience.")
      {'neg': 0.625, 'neu': 0.375, 'pos': 0.0, 'compound': -0.7451}
      """
      import re
      from nltk.stem.snowball import SnowballStemmer
      from nltk.tokenize import word_tokenize
      from nltk.corpus import stopwords
      from nltk.sentiment.vader import SentimentIntensityAnalyzer 
  
      tokens = word_tokenize(text)
      stemmer = SnowballStemmer('english')
      stemmed_words = [stemmer.stem(word) for word in tokens]
      clean_words = [word for word in stemmed_words if re.match('[a-zA-Z-]+$', word)]
      meaningful_words = [word for word in clean_words if not word in stopwords.words('english')]
      sid = SentimentIntensityAnalyzer()
      polarity_score = sid.polarity_scores(' '.join(meaningful_words))
      return polarity_score
  
output:     import re
      from nltk.stem.snowball import SnowballStemmer
      from nltk.tokenize import word_tokenize
      from nltk.corpus import stopwords
      from nltk.sentiment.vader import SentimentIntensityAnalyzer 
  
      tokens = word_tokenize(text)
      stemmer = SnowballStemmer('english')
      stemmed_words = [stemmer.stem(word) for word in tokens]
      clean_words = [word for word in stemmed_words if re.match('[a-zA-Z-]+$', word)]
      meaningful_words = [word for word in clean_words if not word in stopwords.words('english')]
      sid = SentimentIntensityAnalyzer()
      polarity_score = sid.polarity_scores(' '.join(meaningful_words))
      return polarity_score