old_contents: import numpy as np
  
  from typing import Any
  from rasa.nlu.training_data import Message
  from rasa.nlu.components import Component
  from rasa.nlu.constants import MESSAGE_VECTOR_FEATURE_NAMES, MESSAGE_TEXT_ATTRIBUTE
  
  
  class Featurizer(Component):
      @staticmethod
      def _combine_with_existing_features(
          message,
          additional_features,
          feature_name=MESSAGE_VECTOR_FEATURE_NAMES[MESSAGE_TEXT_ATTRIBUTE],
      ):
          if message.get(feature_name) is not None:
              return np.hstack((message.get(feature_name), additional_features))
          else:
              return additional_features
  
new_contents: import numpy as np
  
  from typing import Any, Text
  from rasa.nlu.training_data import Message
  from rasa.nlu.components import Component
  from rasa.nlu.constants import MESSAGE_VECTOR_FEATURE_NAMES, MESSAGE_TEXT_ATTRIBUTE
  
  
  class Featurizer(Component):
      @staticmethod
      def _combine_with_existing_features(
          message: Message,
          additional_features: Any,
          feature_name: Text = MESSAGE_VECTOR_FEATURE_NAMES[MESSAGE_TEXT_ATTRIBUTE],
      ) -> Any:
          if message.get(feature_name) is not None:
              return np.hstack((message.get(feature_name), additional_features))
          else:
              return additional_features
  
subject: add typing to _combine_with_existing_features