input: Write a function to read in a tweet and remove all punctuation, special characters and stopwords. The function should return a list of filtered tokens without the removed elements.
  from nltk.corpus import stopwords
  
  def clean_tweet(tweet):
      tweet_clean = re.sub(r'[^\w\s]','',tweet) #remove punctuation
      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
  
  assert clean_tweet("I'm so excited to be going to the #MuseumTour! #BigDay #travel")==['excited', 'going', 'MuseumTour', 'BigDay', 'travel']
  assert clean_tweet("This is a test tweet with #hashtags, punctuations, and stopwords.")==['test', 'tweet', 'hashtags', 'punctuations', 'stopwords']
  assert clean_tweet("Hello world!")==['Hello', 'world']
  assert clean_tweet("#rare #find #unicorn")==['rare', 'find', 'unicorn']
  assert clean_tweet("")==[]
output: import re
  from nltk.corpus import stopwords
  
  def clean_tweet(tweet):
      tweet_clean = re.sub(r'[^\w\s]','',tweet)
      tokens = tweet_clean.split()
      stop_words = set(stopwords.words("english"))
      filtered_tokens = [w for w in tokens if not w in stop_words]
      return filtered_tokens