input: def clean_tweet(tweet: str) -> list:
      """
      Given a tweet as a string, this function removes all punctuation, special characters, and stopwords,
      and returns the cleaned list of tokens.
  
      >>> clean_tweet("I'm so excited to be going to the #MuseumTour! #BigDay #travel")
      ['excited', 'going', 'MuseumTour', 'BigDay', 'travel']
      """
      import re
      import string
      from nltk.corpus import stopwords
  
      tweet_clean = re.sub(r'[^\w\s]','',tweet)  # remove punctuation and special characters
      tokens = tweet_clean.split()  # split string into individual words
      stop_words = set(stopwords.words("english"))  # get list of stopwords
      filtered_tokens = [w for w in tokens if not w in stop_words]  # remove stopwords
      return filtered_tokens
output:     import re
      import string
      from nltk.corpus import stopwords
  
      tweet_clean = re.sub(r'[^\w\s]','',tweet)  # remove punctuation and special characters
      tokens = tweet_clean.split()  # split string into individual words
      stop_words = set(stopwords.words("english"))  # get list of stopwords
      filtered_tokens = [w for w in tokens if not w in stop_words]  # remove stopwords
      return filtered_tokens