input: 
  def sentiment_analyzer(text: str) -> dict:
      """ Analyzes the sentiment of a given text by tokenizing it, removing stopwords, and determining the polarity score using VADER.
      >>> sentiment_analyzer("I am happy today.")
      {'neg': 0.0, 'neu': 0.591, 'pos': 0.409, 'compound': 0.4404}
      >>> sentiment_analyzer("I am sad and angry.")
      {'neg': 0.677, 'neu': 0.323, 'pos': 0.0, 'compound': -0.7096}
      """
  
output:     from nltk.sentiment.vader import SentimentIntensityAnalyzer
      from nltk.tokenize import word_tokenize
      from nltk.corpus import stopwords
      import nltk
  
      nltk.download('vader_lexicon')
      nltk.download('punkt')
      nltk.download('stopwords')
  
      analyzer = SentimentIntensityAnalyzer()
      stop_words = set(stopwords.words('english'))
  
      tokens = word_tokenize(text.lower())
      filtered_tokens = [word for word in tokens if word.isalnum() and word not in stop_words]
      filtered_text = ' '.join(filtered_tokens)
  
      sentiment_scores = analyzer.polarity_scores(filtered_text)
      return sentiment_scores