input: Write a function to create a machine learning classifier in Python for categorizing user input using a Multinomial Naive Bayes algorithm. The function should extract features from the input string, train the classifier, and return the prediction.
  assert classify_string("example input 1")==[2]
  assert classify_string("another example input")==[1]
  assert classify_string("test input")==[3]
  assert classify_string("final test input")==[4]
  assert classify_string("short input")==[0]
output: def classify_string(input_string):
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      import numpy as np
  
      # Sample data for training
      data = [
          "short input",
          "another example input",
          "example input 1",
          "test input",
          "final test input"
      ]
      labels = [0, 1, 2, 3, 4]
  
      # Feature extraction
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(data)
  
      # Train the classifier
      classifier = MultinomialNB()
      classifier.fit(X, labels)
  
      # Transform the input string
      input_vector = vectorizer.transform([input_string])
  
      # Predict the category
      prediction = classifier.predict(input_vector)
  
      return prediction.tolist()