input: def classify_string(input_string: str) -> str:
      """Creates a machine learning classifier in Python for categorizing user input.
      The function uses a Naive Bayes classifier to predict the category of the input string.
  
      Example:
      >>> classify_string("This is a test sentence.")
      'Category 1'
      >>> classify_string("Another example of input.")
      'Category 2'
      """
      
      # Feature extraction and transformation from string to numeric
      features = ...
      
      # Create an instance of the classifier 
      model = MultinomialNB()
      
      # Train the classifier on the data
      model.fit(data, target)
      
      # Make a prediction based on the input
      prediction = model.predict([features])
      
      # Return the prediction
      return prediction
output:     from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
  
      # Sample data for training
      data = ["This is a test sentence.", "Another example of input."]
      target = ["Category 1", "Category 2"]
  
      # Feature extraction and transformation from string to numeric
      vectorizer = CountVectorizer()
      features = vectorizer.fit_transform(data).toarray()
  
      # Create an instance of the classifier 
      model = MultinomialNB()
  
      # Train the classifier on the data
      model.fit(features, target)
  
      # Transform the input string to numeric features
      input_features = vectorizer.transform([input_string]).toarray()
  
      # Make a prediction based on the input
      prediction = model.predict(input_features)
  
      # Return the prediction
      return prediction[0]