input: from typing import Dict
  
  def sentiment_analyzer(text: str) -> Dict[str, float]:
      """ Analyzes the sentiment of a given text by tokenizing, stemming, removing stopwords and punctuation, 
      and then calculating the polarity score using VADER SentimentIntensityAnalyzer.
  
      >>> sentiment_analyzer('Today is a beautiful day!')
      {'neg': 0.0, 'neu': 0.356, 'pos': 0.644, 'compound': 0.6249}
      >>> sentiment_analyzer('I feel sad and lonely.')
      {'neg': 0.625, 'neu': 0.375, 'pos': 0.0, 'compound': -0.4767}
      """
output:     from nltk.tokenize import word_tokenize
      from nltk.stem import PorterStemmer
      from nltk.corpus import stopwords
      from nltk.sentiment import SentimentIntensityAnalyzer
      import string
  
      # Tokenize the text
      tokens = word_tokenize(text.lower())
  
      # Remove stopwords and punctuation
      stop_words = set(stopwords.words('english'))
      filtered_tokens = [word for word in tokens if word.isalnum() and word not in stop_words]
  
      # Stem the tokens
      stemmer = PorterStemmer()
      stemmed_tokens = [stemmer.stem(word) for word in filtered_tokens]
  
      # Join the stemmed tokens back into a single string
      processed_text = ' '.join(stemmed_tokens)
  
      # Calculate sentiment scores using VADER
      sia = SentimentIntensityAnalyzer()
      sentiment_scores = sia.polarity_scores(processed_text)
  
      return sentiment_scores