input: 
  from typing import Dict
  from nltk.stem.snowball import SnowballStemmer
  from nltk.tokenize import word_tokenize
  from nltk.corpus import stopwords
  from nltk.sentiment.vader import SentimentIntensityAnalyzer
  import re
  
  def sentiment_analyzer(text: str) -> Dict[str, float]:
      """ Analyze the sentiment of a given text. The function first tokenizes the text, 
      then stems each token, removes punctuation, and removes stopwords. Finally, it 
      calculates the polarity score of the text using the VADER sentiment analysis tool.
      >>> sentiment_analyzer("I love pizza!")
      {'neg': 0.0, 'neu': 0.367, 'pos': 0.633, 'compound': 0.6369}
      >>> sentiment_analyzer("I hate Mondays.")
      {'neg': 0.649, 'neu': 0.351, 'pos': 0.0, 'compound': -0.5423}
      >>> sentiment_analyzer("Life is good.")
      {'neg': 0.0, 'neu': 0.391, 'pos': 0.609, 'compound': 0.4404}
      """
      # Tokenize the text
      tokens = word_tokenize(text)
  
      # Create stemmer object
      stemmer = SnowballStemmer('english')
  
      # Stem each token
      stemmed_words = [stemmer.stem(word) for word in tokens]
  
      # Remove punctuation
      clean_words = [word for word in stemmed_words if re.match('[a-zA-Z-]+$', word)]
  
      # Remove stopwords
      meaningful_words = [word for word in clean_words if word not in stopwords.words('english')]
  
      # Create a SentimentIntensityAnalyzer object
      sid = SentimentIntensityAnalyzer()
  
      # Get the polarity score of the text
      polarity_score = sid.polarity_scores(' '.join(meaningful_words))
  
      return polarity_score
  
output:     # Tokenize the text
      tokens = word_tokenize(text)
  
      # Create stemmer object
      stemmer = SnowballStemmer('english')
  
      # Stem each token
      stemmed_words = [stemmer.stem(word) for word in tokens]
  
      # Remove punctuation
      clean_words = [word for word in stemmed_words if re.match('[a-zA-Z-]+$', word)]
  
      # Remove stopwords
      meaningful_words = [word for word in clean_words if word not in stopwords.words('english')]
  
      # Create a SentimentIntensityAnalyzer object
      sid = SentimentIntensityAnalyzer()
  
      # Get the polarity score of the text
      polarity_score = sid.polarity_scores(' '.join(meaningful_words))
  
      return polarity_score