input: from typing import List
  
  def classify_string(input_string: str) -> str:
      """
      Create a machine learning classifier in Python for categorizing user input.
      The function assumes that the input string is transformed into features, 
      which are then used to train a Multinomial Naive Bayes classifier. The 
      classifier predicts the category of the input string based on the trained model.
      
      >>> classify_string("Example input string")
      'predicted_category'
      """
output:     from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.pipeline import make_pipeline
  
      # Example training data
      train_data = ["Example input string", "Another example", "Yet another example"]
      train_labels = ["category1", "category2", "category1"]
  
      # Create a pipeline that transforms text to vectors and trains a classifier
      model = make_pipeline(CountVectorizer(), MultinomialNB())
      model.fit(train_data, train_labels)
  
      # Predict the category of the input string
      predicted_category = model.predict([input_string])[0]
      return predicted_category