input: from typing import List
  
  def classify_string(input_string: str) -> str:
      """
      Create a machine learning classifier in Python for categorizing user input.
      This function takes an input string, extracts its features, trains a Multinomial Naive Bayes classifier,
      and returns the predicted category of the input string.
  
      Example:
      >>> classify_string("This is a test message")
      'test'
      >>> classify_string("Another example string")
      'example'
      """
output:     from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.pipeline import make_pipeline
  
      # Sample training data
      data = [
          ("This is a test message", "test"),
          ("Another example string", "example"),
          ("This is a sample message", "sample"),
          ("Another test string", "test"),
          ("Example of a message", "example"),
          ("Sample message here", "sample")
      ]
  
      # Split data into features and labels
      X_train = [d[0] for d in data]
      y_train = [d[1] for d in data]
  
      # Create a pipeline with CountVectorizer and MultinomialNB
      model = make_pipeline(CountVectorizer(), MultinomialNB())
  
      # Train the model
      model.fit(X_train, y_train)
  
      # Predict the category of the input string
      return model.predict([input_string])[0]