input: Write a function to implement a natural language response system using a TensorFlow-based neural network model. The function should accept a sample utterance and return the generated response based on the trained model. If the model is not trained, it should train the model before generating the response.
  assert create_response("What time is it?") == "<predicted_response>"
  assert create_response("How are you?") == "<predicted_response>"
  assert create_response("Hello") == "<predicted_response>"
  assert create_response("Goodbye") == "<predicted_response>"
  assert create_response("What's your name?") == "<predicted_response>"
output: def create_response(utterance):
      # Placeholder for the TensorFlow-based neural network model
      class Model:
          def __init__(self):
              self.trained = False
  
          def train(self):
              # Placeholder for training logic
              self.trained = True
  
          def predict(self, utterance):
              # Placeholder for prediction logic
              return f"<predicted_response_for_{utterance}>"
  
      model = Model()
      if not model.trained:
          model.train()
      return model.predict(utterance)