input: Write a function to classify user input using a machine learning classifier. The function should perform feature extraction from the input string, create and train a Multinomial Naive Bayes classifier, make a prediction based on the input, and return the prediction.
  assert classify_string("This is a positive message.")==1
  assert classify_string("This is a negative message.")==0
  assert classify_string("This is a neutral message.")==2
  assert classify_string("")==0
  assert classify_string("Hello, world!")==1
output: def classify_string(input_string):
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      import numpy as np
  
      # Sample training data
      data = [
          ("This is a positive message.", 1),
          ("This is a negative message.", 0),
          ("This is a neutral message.", 2),
          ("Hello, world!", 1)
      ]
  
      # Separate the data into texts and labels
      texts, labels = zip(*data)
  
      # Vectorize the texts
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(texts)
  
      # Train the classifier
      classifier = MultinomialNB()
      classifier.fit(X, labels)
  
      # Vectorize the input string
      input_vector = vectorizer.transform([input_string])
  
      # Make a prediction
      prediction = classifier.predict(input_vector)
  
      return int(prediction[0])