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, train the model on the data, and return the prediction. If the input is empty, return an appropriate error message.
  assert classify_string("example input") == "some_category"
  assert classify_string("") == "Error: Input string is empty"
  assert classify_string("another example") == "another_category"
  assert classify_string("third input") == "third_category"
  assert classify_string("final input") == "final_category"
output: def classify_string(input_string):
      if not input_string:
          return "Error: Input string is empty"
      
      # Example data for training
      data = [
          ("example input", "some_category"),
          ("another example", "another_category"),
          ("third input", "third_category"),
          ("final input", "final_category")
      ]
      
      # Extract features and labels
      X = [text for text, _ in data]
      y = [label for _, label in data]
      
      # Vectorize the text data
      vectorizer = CountVectorizer()
      X_train = vectorizer.fit_transform(X)
      
      # Train the model
      model = MultinomialNB()
      model.fit(X_train, y)
      
      # Vectorize the input string
      input_vector = vectorizer.transform([input_string])
      
      # Predict the category
      prediction = model.predict(input_vector)
      
      return prediction[0]