input: from typing import List
  
  def sentiment_analyzer(text: str) -> dict:
      """Analyze the sentiment of a given text. The function tokenizes the text, stems each token, removes punctuation and stopwords, and then calculates the polarity score using SentimentIntensityAnalyzer.
      >>> sentiment_analyzer("I love this product!")
      {'neg': 0.0, 'neu': 0.295, 'pos': 0.705, 'compound': 0.6114}
      >>> sentiment_analyzer("This is a terrible experience.")
      {'neg': 0.593, 'neu': 0.407, 'pos': 0.0, 'compound': -0.6249}
      """
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 punctuation and stopwords
      tokens = [token for token in tokens if token not in string.punctuation]
      stop_words = set(stopwords.words('english'))
      tokens = [token for token in tokens if token not in stop_words]
  
      # Stem each token
      stemmer = PorterStemmer()
      tokens = [stemmer.stem(token) for token in tokens]
  
      # Calculate sentiment scores
      sia = SentimentIntensityAnalyzer()
      sentiment_scores = sia.polarity_scores(' '.join(tokens))
  
      return sentiment_scores