input: from typing import Dict
  
  def sentiment_analyzer(text: str) -> Dict[str, float]:
      """Analyzes the sentiment of a given text by processing the text, removing stopwords, and calculating a sentiment score using VADER.
      >>> sentiment_analyzer("I love programming! It's the best thing ever.")
      {'neg': 0.0, 'neu': 0.373, 'pos': 0.627, 'compound': 0.8225}
      >>> sentiment_analyzer("I hate Mondays. They are so boring and tiring.")
      {'neg': 0.469, 'neu': 0.531, 'pos': 0.0, 'compound': -0.5622}
      """
output:     from nltk.sentiment.vader import SentimentIntensityAnalyzer
      from nltk.corpus import stopwords
      from nltk.tokenize import word_tokenize
  
      # Initialize the sentiment analyzer
      analyzer = SentimentIntensityAnalyzer()
  
      # Tokenize the text and remove stopwords
      words = word_tokenize(text.lower())
      filtered_words = [word for word in words if word.isalnum() and word not in stopwords.words('english')]
  
      # Join the filtered words back into a single string
      filtered_text = ' '.join(filtered_words)
  
      # Get the sentiment scores
      sentiment_scores = analyzer.polarity_scores(filtered_text)
  
      return sentiment_scores