input: 
  def classify_string(input_string):
      """Create a machine learning classifier to categorize user input. The classifier uses a Naive Bayes model from sklearn.
      >>> classify_string("This is a test")
      'category_a'
      >>> classify_string("Another example")
      'category_b'
      """
  
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", "category_a"),
          ("Another example", "category_b"),
          ("Yet another test", "category_a"),
          ("Different category", "category_b")
      ]
  
      # Split into features and labels
      X_train = [x[0] for x in train_data]
      y_train = [x[1] for x in train_data]
  
      # Create a pipeline with CountVectorizer and MultinomialNB
      model = make_pipeline(CountVectorizer(), MultinomialNB())
      model.fit(X_train, y_train)
  
      # Predict the category of the input string
      return model.predict([input_string])[0]
  