input: def classify_string(input_string: str) -> str:
      """Create a machine learning classifier using MultinomialNB to categorize user input.
      The function should transform the input string into numeric features and return a prediction.
      
      >>> classify_string("This is a test sentence.")
      'category_1'
      
      >>> classify_string("Another example of user input.")
      'category_2'
      """
output:     from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.pipeline import make_pipeline
  
      # Sample training data
      train_data = [
          ("This is a test sentence.", "category_1"),
          ("Another example of user input.", "category_2"),
          ("This is another test.", "category_1"),
          ("User input example.", "category_2")
      ]
  
      # Split data into features and labels
      X_train = [text for text, _ in train_data]
      y_train = [label for _, label in train_data]
  
      # Create a pipeline with CountVectorizer and MultinomialNB
      model = make_pipeline(CountVectorizer(), MultinomialNB())
  
      # Train the model
      model.fit(X_train, y_train)
  
      # Transform the input string and predict the category
      return model.predict([input_string])[0]