old_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_SPARSE_FEATURE_NAMES,
      MESSAGE_TEXT_ATTRIBUTE,
  )
  
  
  class Featurizer(Component):
      @staticmethod
      def _combine_with_existing_dense_features(
          message: Message,
          additional_features: Any,
          feature_name: Text = MESSAGE_VECTOR_SPARSE_FEATURE_NAMES[
              MESSAGE_TEXT_ATTRIBUTE
          ],
      ) -> Any:
          if message.get(feature_name) is not None:
              return np.concatenate(
                  (message.get(feature_name), additional_features), axis=-1
              )
          else:
              return additional_features
  
      @staticmethod
      def _combine_with_existing_sparse_features(
          message: Message,
          additional_features: Any,
          feature_name: Text = MESSAGE_VECTOR_SPARSE_FEATURE_NAMES[
              MESSAGE_TEXT_ATTRIBUTE
          ],
      ) -> Any:
          if message.get(feature_name) is not None:
              from scipy.sparse import hstack
  
              return 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_SPARSE_FEATURE_NAMES,
      MESSAGE_VECTOR_DENSE_FEATURE_NAMES,
      MESSAGE_TEXT_ATTRIBUTE,
  )
  
  
  class Featurizer(Component):
      @staticmethod
      def _combine_with_existing_dense_features(
          message: Message,
          additional_features: Any,
          feature_name: Text = MESSAGE_VECTOR_DENSE_FEATURE_NAMES[MESSAGE_TEXT_ATTRIBUTE],
      ) -> Any:
          if message.get(feature_name) is not None:
              return np.concatenate(
                  (message.get(feature_name), additional_features), axis=-1
              )
          else:
              return additional_features
  
      @staticmethod
      def _combine_with_existing_sparse_features(
          message: Message,
          additional_features: Any,
          feature_name: Text = MESSAGE_VECTOR_SPARSE_FEATURE_NAMES[
              MESSAGE_TEXT_ATTRIBUTE
          ],
      ) -> Any:
          if message.get(feature_name) is not None:
              from scipy.sparse import hstack
  
              return hstack([message.get(feature_name), additional_features])
          else:
              return additional_features
  
subject: change default value