input: def classify_string(input_string: str) -> str:
      """
      Given a string input, this function extracts features from the string,
      trains a Multinomial Naive Bayes classifier on pre-labeled data, and
      returns the predicted category of the input string.
      >>> classify_string("This is a positive review")
      'positive'
      >>> classify_string("This is a negative review")
      'negative'
      """
output:     from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.pipeline import make_pipeline
  
      # Pre-labeled data
      data = [
          ("This is a positive review", "positive"),
          ("This is a negative review", "negative"),
          ("I love this product", "positive"),
          ("I hate this product", "negative"),
          ("This is amazing", "positive"),
          ("This is terrible", "negative")
      ]
  
      # Split data into features and labels
      texts, labels = zip(*data)
  
      # Create a pipeline with CountVectorizer and MultinomialNB
      model = make_pipeline(CountVectorizer(), MultinomialNB())
  
      # Train the model
      model.fit(texts, labels)
  
      # Predict the category of the input string
      return model.predict([input_string])[0]