input: Write a python function to fetch tweets containing a specific keyword and analyze their sentiment.
  assert fetch_and_analyze_tweets('Python programming') == 'Function should output tweets and their sentiment analysis.'
  assert fetch_and_analyze_tweets('Data Science') == 'Function should output tweets and their sentiment analysis.'
  assert fetch_and_analyze_tweets('Machine Learning') == 'Function should output tweets and their sentiment analysis.'
output: def fetch_and_analyze_tweets(keyword):
      import tweepy
      from textblob import TextBlob
  
      # Set up the Twitter API client
      consumer_key = 'your_consumer_key'
      consumer_secret = 'your_consumer_secret'
      access_token = 'your_access_token'
      access_token_secret = 'your_access_token_secret'
  
      auth = tweepy.OAuth1UserHandler(consumer_key, consumer_secret, access_token, access_token_secret)
      api = tweepy.API(auth)
  
      # Fetch tweets containing the keyword
      tweets = api.search(q=keyword, count=100, lang='en')
  
      # Analyze sentiment of each tweet
      tweet_sentiments = []
      for tweet in tweets:
          analysis = TextBlob(tweet.text)
          sentiment = 'positive' if analysis.sentiment.polarity > 0 else 'negative' if analysis.sentiment.polarity < 0 else 'neutral'
          tweet_sentiments.append((tweet.text, sentiment))
  
      # Output tweets and their sentiment analysis
      return tweet_sentiments