input: Write a function to read a tweet and remove all punctuation, special characters, and stopwords. The function should return the cleaned tweet as a list of tokens.
  assert remove_stopwords("I'm so excited to be going to the #MuseumTour! #BigDay #travel")==['excited', 'going', 'MuseumTour', 'BigDay', 'travel']
  assert remove_stopwords("Can't wait to see the new exhibition at the museum!")==['Can', 'wait', 'see', 'new', 'exhibition', 'museum']
  assert remove_stopwords("Hello world! This is a test message.")==['Hello', 'world', 'test', 'message']
  assert remove_stopwords("I love learning new things!")==['love', 'learning', 'new', 'things']
  assert remove_stopwords("")==[]
output: def remove_stopwords(tweet):
      import re
      from nltk.corpus import stopwords
      stop_words = set(stopwords.words('english'))
      tweet = re.sub(r'[^a-zA-Z\s]', '', tweet)
      tokens = tweet.split()
      cleaned_tokens = [word for word in tokens if word.lower() not in stop_words]
      return cleaned_tokens