input: def sentiment_analyzer(text: str) -> dict:
      """
      Analyzes the sentiment of a given text by tokenizing it, removing stopwords and punctuation, 
      stemming the remaining words, and then using the VADER sentiment analysis tool to determine 
      the sentiment polarity.
  
      >>> sentiment_analyzer("I am very happy today!")
      {'neg': 0.0, 'neu': 0.239, 'pos': 0.761, 'compound': 0.8439}
      >>> sentiment_analyzer("I am sad and frustrated.")
      {'neg': 0.625, 'neu': 0.375, 'pos': 0.0, 'compound': -0.7042}
      """
      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 
  
      # 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:     return polarity_score