input: def sentiment_analyzer(text: str) -> dict:
      """ Analyzes the sentiment of a given text by tokenizing it, removing stopwords,
      stemming the words, and then computing the polarity score of the cleaned text.
      >>> sentiment_analyzer("I love this product! It is amazing.")
      {'neg': 0.0, 'neu': 0.348, 'pos': 0.652, 'compound': 0.6249}
      >>> sentiment_analyzer("This is the worst experience I've ever had.")
      {'neg': 0.417, 'neu': 0.583, 'pos': 0.0, 'compound': -0.5423}
      """
output:     from nltk.tokenize import word_tokenize
      from nltk.corpus import stopwords
      from nltk.stem import PorterStemmer
      from nltk.sentiment import SentimentIntensityAnalyzer
  
      # Tokenize the text
      tokens = word_tokenize(text.lower())
  
      # Remove stopwords
      stop_words = set(stopwords.words('english'))
      filtered_tokens = [word for word in tokens if word.isalnum() and word not in stop_words]
  
      # Stem the words
      stemmer = PorterStemmer()
      stemmed_tokens = [stemmer.stem(word) for word in filtered_tokens]
  
      # Join the stemmed tokens back into a single string
      cleaned_text = ' '.join(stemmed_tokens)
  
      # Compute the sentiment scores
      sia = SentimentIntensityAnalyzer()
      sentiment_scores = sia.polarity_scores(cleaned_text)
  
      return sentiment_scores