input: 
  def visually_summarize_twitter_data(twitter_data):
      """
      Visually summarizes the Twitter data by plotting various metrics using matplotlib.
      The function processes the Twitter data to extract relevant fields such as tweet authors,
      number of likes, and number of retweets. It then generates three plots: one for tweet
      authors, one for the number of likes, and one for the number of retweets.
      >>> visually_summarize_twitter_data([{'author': 'user1', 'likes': 10, 'retweets': 5}, 
                                           {'author': 'user2', 'likes': 20, 'retweets': 10}, 
                                           {'author': 'user1', 'likes': 15, 'retweets': 7}])
      # This will generate three plots: one for tweet authors, one for the number of likes, 
      # and one for the number of retweets.
      """
  
output:     import matplotlib.pyplot as plt
      from collections import Counter
  
      authors = [tweet['author'] for tweet in twitter_data]
      likes = [tweet['likes'] for tweet in twitter_data]
      retweets = [tweet['retweets'] for tweet in twitter_data]
  
      author_counts = Counter(authors)
      author_labels, author_values = zip(*author_counts.items())
  
      plt.figure(figsize=(15, 5))
  
      plt.subplot(1, 3, 1)
      plt.bar(author_labels, author_values)
      plt.xlabel('Authors')
      plt.ylabel('Number of Tweets')
      plt.title('Tweet Authors')
  
      plt.subplot(1, 3, 2)
      plt.hist(likes, bins=10, edgecolor='black')
      plt.xlabel('Number of Likes')
      plt.ylabel('Frequency')
      plt.title('Likes Distribution')
  
      plt.subplot(1, 3, 3)
      plt.hist(retweets, bins=10, edgecolor='black')
      plt.xlabel('Number of Retweets')
      plt.ylabel('Frequency')
      plt.title('Retweets Distribution')
  
      plt.tight_layout()
      plt.show()