input: from typing import Dict
  
  def sentiment_analyzer(text: str) -> Dict[str, float]:
      """
      Perform sentiment analysis on the given text and return the polarity scores as a dictionary.
  
      This function tokenizes the input text, stems each token, removes punctuation,
      and filters out stopwords before computing the polarity scores using VADER sentiment analysis.
  
      >>> sentiment_analyzer("I absolutely love this product! It's just amazing.")
      {'neg': 0.0, 'neu': 0.265, 'pos': 0.735, 'compound': 0.8516}
      >>> sentiment_analyzer("The food was terrible, I hated it.")
      {'neg': 0.661, 'neu': 0.339, 'pos': 0.0, 'compound': -0.6908}
      """
output:     from nltk.sentiment.vader import SentimentIntensityAnalyzer
      from nltk.tokenize import word_tokenize
      from nltk.stem import PorterStemmer
      from nltk.corpus import stopwords
      import string
  
      # Initialize the sentiment analyzer
      analyzer = SentimentIntensityAnalyzer()
      stemmer = PorterStemmer()
      stop_words = set(stopwords.words('english'))
  
      # Tokenize the text
      tokens = word_tokenize(text.lower())
  
      # Remove punctuation and stopwords, and stem the tokens
      filtered_tokens = [stemmer.stem(token) for token in tokens if token.isalnum() and token not in stop_words]
  
      # Join the filtered tokens back into a single string
      filtered_text = ' '.join(filtered_tokens)
  
      # Get the polarity scores
      sentiment_scores = analyzer.polarity_scores(filtered_text)
  
      return sentiment_scores