input: def sentiment_analyzer(text: str) -> dict:
      """ Analyzes the sentiment of a given text by tokenizing it, stemming each token,
      removing punctuation and stopwords, and finally assessing the sentiment polarity.
      >>> sentiment_analyzer("I love programming!")
      {'neg': 0.0, 'neu': 0.35, 'pos': 0.65, 'compound': 0.6249}
      >>> sentiment_analyzer("I really hate unexpected errors.")
      {'neg': 0.632, 'neu': 0.368, 'pos': 0.0, 'compound': -0.7424}
      """
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
      tokens = [token for token in tokens if token not in string.punctuation]
  
      # Remove stopwords
      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]
  
      # Join tokens back into a single string
      processed_text = ' '.join(tokens)
  
      # Analyze sentiment
      sia = SentimentIntensityAnalyzer()
      sentiment_scores = sia.polarity_scores(processed_text)
  
      return sentiment_scores