input: 
  from typing import Dict
  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 
  
  def sentiment_analyzer(text: str) -> Dict[str, float]:
      """ Analyze the sentiment of the given text using the VADER sentiment analyzer.
      The function tokenizes the text, removes stopwords, and stems the remaining words.
      It then calculates the sentiment polarity score for the cleaned text.
      
      >>> sentiment_analyzer("I love programming but it's so challenging!")
      {'neg': 0.254, 'neu': 0.561, 'pos': 0.185, 'compound': 0.4019}
      """
  
      # 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 not word 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