input: 
  from typing import List
  
  
  def classify_string(input_string: str, data: List[List[float]], target: List[int]) -> int:
      """ Classify a given input string using a Multinomial Naive Bayes classifier.
      The function assumes that the input string has already been preprocessed and
      transformed into numerical features, which are used to train the model.
      The model returns the predicted category for the input string.
      >>> classify_string("example input", [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], [0, 1])
      0
      >>> classify_string("another example", [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], [1, 0])
      1
      """
output:     import numpy as np
      from sklearn.naive_bayes import MultinomialNB
  
      # Convert data to numpy array
      X = np.array(data)
      y = np.array(target)
  
      # Train the Multinomial Naive Bayes classifier
      clf = MultinomialNB()
      clf.fit(X, y)
  
      # Convert input_string to numerical features (assuming it's already preprocessed)
      input_features = np.array([float(x) for x in input_string.split()]).reshape(1, -1)
  
      # Predict the category
      predicted_category = clf.predict(input_features)
  
      return predicted_category[0]