input: def classify_string(input_string: str) -> str:
      """
      Create a machine learning classifier using MultinomialNB for categorizing user input.
      Given an input string, the function performs feature extraction, trains the classifier,
      and returns the category prediction.
      
      >>> classify_string('This is a sample string')
      'CategoryA'
      >>> classify_string('This is another example')
      'CategoryB'
      """
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 sample string', 'CategoryA'),
          ('This is another example', 'CategoryB'),
          ('Yet another string', 'CategoryA'),
          ('One more example', 'CategoryB')
      ]
  
      # 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]