input: Write a python function to fetch and analyze the sentiment of tweets using the Tweepy and TextBlob libraries.
  assert len(fetch_and_analyze_tweets('#python', 10)) == 10
  assert all(isinstance(polarity, float) for polarity in fetch_and_analyze_tweets('#datascience', 5))
  assert sum(fetch_and_analyze_tweets('#machinelearning', 3)) != 0
output: import tweepy
  from textblob import TextBlob
  
  def fetch_and_analyze_tweets(keyword, count):
      # Replace with your own credentials
      consumer_key = 'YOUR_CONSUMER_KEY'
      consumer_secret = 'YOUR_CONSUMER_SECRET'
      access_token = 'YOUR_ACCESS_TOKEN'
      access_token_secret = 'YOUR_ACCESS_TOKEN_SECRET'
  
      # Authenticate to Twitter
      auth = tweepy.OAuth1UserHandler(consumer_key, consumer_secret, access_token, access_token_secret)
      api = tweepy.API(auth)
  
      # Fetch tweets
      tweets = tweepy.Cursor(api.search_tweets, q=keyword, lang="en").items(count)
  
      # Analyze sentiment
      tweet_polarities = []
      for tweet in tweets:
          analysis = TextBlob(tweet.text)
          tweet_polarities.append(analysis.sentiment.polarity)
  
      return tweet_polarities