input: Write a python function to classify a string using a Naive Bayes classifier.
  assert classify_string("example string") == "predicted_class"
  assert classify_string("another example") == "another_predicted_class"
  assert classify_string("yet another example") == "yet_another_predicted_class"
output: def classify_string(input_string):
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      import numpy as np
  
      # Dummy training data and labels
      training_data = ["example string", "another example", "yet another example"]
      training_labels = ["predicted_class", "another_predicted_class", "yet_another_predicted_class"]
  
      # Create a CountVectorizer to convert text data into vectors
      vectorizer = CountVectorizer()
      X_train = vectorizer.fit_transform(training_data)
  
      # Train a Naive Bayes classifier
      classifier = MultinomialNB()
      classifier.fit(X_train, training_labels)
  
      # Transform the input string using the same vectorizer
      X_input = vectorizer.transform([input_string])
  
      # Predict the class of the input string
      predicted_class = classifier.predict(X_input)[0]
  
      return predicted_class