input: from typing import Dict
  
  def sentiment_analyzer(text: str) -> Dict[str, float]:
      """ Analyze the sentiment of a given text by tokenizing it, stemming the words, 
      removing punctuation and stopwords, and calculating the polarity score using 
      the VADER sentiment analysis tool.
      >>> sentiment_analyzer("I love this product! It's amazing.")
      {'neg': 0.0, 'neu': 0.292, 'pos': 0.708, 'compound': 0.8316}
      >>> sentiment_analyzer("This is a terrible experience. I hate it.")
      {'neg': 0.657, 'neu': 0.343, 'pos': 0.0, 'compound': -0.7845}
      """
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 words
      filtered_tokens = [stemmer.stem(word) for word in tokens if word.isalnum() and word not in stop_words]
  
      # Join the filtered tokens back into a single string
      filtered_text = ' '.join(filtered_tokens)
  
      # Calculate the sentiment scores
      sentiment_scores = analyzer.polarity_scores(filtered_text)
  
      return sentiment_scores