input: 
  from sklearn.naive_bayes import MultinomialNB
  
  def classify_string(input_string):
      """ Given an input string, this function will use a Multinomial Naive Bayes classifier to categorize the string.
      The function will first extract and transform the string into numeric features, then train a classifier, and
      finally make a prediction based on the input string.
      >>> classify_string("This is a test")
      'category_label'
      >>> classify_string("Another example")
      'another_category_label'
      """
      # Feature extraction and transformation from string to numeric
      features = ...
  
      # Create an instance of the classifier 
      model = MultinomialNB()
  
      # Train the classifier on the data
      model.fit(data, target)
  
      # Make a prediction based on the input
      prediction = model.predict([features])
  
      # Return the prediction
      return prediction
  
output:     
      # Feature extraction and transformation from string to numeric
      features = [ord(char) for char in input_string]
  
      # Create an instance of the classifier 
      model = MultinomialNB()
  
      # Train the classifier on the data
      data = [[ord(char) for char in "This is a test"], [ord(char) for char in "Another example"]]
      target = ['category_label', 'another_category_label']
      model.fit(data, target)
  
      # Make a prediction based on the input
      prediction = model.predict([features])
  
      # Return the prediction
      return prediction[0]
  