input: 
  def sentiment_analyzer(text: str) -> dict:
      """Analyzes the sentiment of given text by tokenizing, stemming, removing stopwords,
      and calculating the sentiment polarity score.
  
      >>> sentiment_analyzer("I love this product!")
      {'neg': 0.0, 'neu': 0.353, 'pos': 0.647, 'compound': 0.6369}
      >>> sentiment_analyzer("I hate this product!!")
      {'neg': 0.671, 'neu': 0.329, 'pos': 0.0, 'compound': -0.7347}
      """
  
output:     from nltk.sentiment import SentimentIntensityAnalyzer
      from nltk.tokenize import word_tokenize
      from nltk.stem import PorterStemmer
      from nltk.corpus import stopwords
      import string
  
      sia = SentimentIntensityAnalyzer()
      stemmer = PorterStemmer()
      stop_words = set(stopwords.words('english'))
  
      def preprocess(text):
          tokens = word_tokenize(text.lower())
          tokens = [stemmer.stem(word) for word in tokens if word.isalnum() and word not in stop_words and word not in string.punctuation]
          return ' '.join(tokens)
  
      processed_text = preprocess(text)
      sentiment_scores = sia.polarity_scores(processed_text)
      
      return sentiment_scores