input: Write a function to create a machine learning classifier in Python for categorizing user input using the Multinomial Naive Bayes algorithm. The function should take an input string, extract features from it, train the classifier, and return the predicted category.
  assert classify_string("example input 1")==[0]
  assert classify_string("example input 2")==[1]
  assert classify_string("example input 3")==[0]
  assert classify_string("example input 4")==[2]
  
  
  
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
      data = ["example input 1", "example input 2", "example input 3", "example input 4"]
      labels = [0, 1, 0, 2]
  
      # Feature extraction
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(data)
  
      # Train the classifier
      clf = MultinomialNB()
      clf.fit(X, labels)
  
      # Transform the input string
      input_vector = vectorizer.transform([input_string])
  
      # Predict the category
      predicted_category = clf.predict(input_vector)
  
      return predicted_category.tolist()