input: from typing import Dict
  from nltk.stem.snowball import SnowballStemmer
  from nltk.tokenize import word_tokenize
  from nltk.corpus import stopwords
  from nltk.sentiment.vader import SentimentIntensityAnalyzer
  
  def sentiment_analyzer(text: str) -> Dict[str, float]:
      """ Analyze the sentiment of a given text by tokenizing it, removing stopwords,
      stemming the remaining words, and then calculating the polarity score using 
      the VADER SentimentIntensityAnalyzer.
      >>> sentiment_analyzer("I love this product! It's amazing.")
      {'neg': 0.0, 'neu': 0.295, 'pos': 0.705, 'compound': 0.6369}
      >>> sentiment_analyzer("This movie was horrible, I hated it.")
      {'neg': 0.546, 'neu': 0.454, 'pos': 0.0, 'compound': -0.5994}
      """
output:     stemmer = SnowballStemmer("english")
      stop_words = set(stopwords.words("english"))
      analyzer = SentimentIntensityAnalyzer()
  
      tokens = word_tokenize(text.lower())
      filtered_tokens = [stemmer.stem(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