class_docstr: Container for data that can be used to describe a conversation turn.
  
  The turn is described by a set of attributes such as e.g. `TEXT`  and  `INTENT`
  when describing a user utterance or e.g. `ACTION_NAME` for describing a bot action.
  The container includes raw information (`self.data`) as well as features
  (`self.features`) for each such attribute.
  Moreover, the message has a timestamp and can keep track about information
  on a specific subset of attributes (`self.output_properties`).
docstr: Gets all sparse features for the attribute given the list of featurizers.
  
  If no featurizers are provided, all available features will be considered.
  
  Args:
      attribute: message attribute
      featurizers: names of featurizers to consider
  
  Returns:
      Sparse features.
docstr_tok: ["Gets","all","sparse","features","for","the","attribute","given","the","list","of","featurizers",".","If","no","featurizers","are","provided",",","all","available","features","will","be","considered",".","Args",":","attribute",":","message","attribute","featurizers",":","names","of","featurizers","to","consider","Returns",":","Sparse","features","."]
code: def get_sparse_features(
      self, attribute: Text, featurizers: Optional[List[Text]] = None
  ) -> Tuple[Optional["Features"], Optional["Features"]]:
      """Gets all sparse features for the attribute given the list of featurizers.
  
      If no featurizers are provided, all available features will be considered.
  
      Args:
          attribute: message attribute
          featurizers: names of featurizers to consider
  
      Returns:
          Sparse features.
      """
      if featurizers is None:
          featurizers = []
  
      (
          sequence_features,
          sentence_features,
      ) = self._filter_sparse_features(attribute, featurizers)
  
      combined_sequence_features = self._combine_features(
          sequence_features, featurizers
      )
      combined_sentence_features = self._combine_features(
          sentence_features, featurizers
      )
  
      return combined_sequence_features, combined_sentence_features
  
imports: from typing import Any, Optional, Tuple, Text, Dict, Set, List
  import typing
  import copy
  import rasa.shared.utils.io
  from rasa.shared.exceptions import RasaException
  from rasa.shared.nlu.constants import TEXT, INTENT, RESPONSE, INTENT_RESPONSE_KEY, METADATA, METADATA_INTENT, METADATA_EXAMPLE, ENTITIES, ENTITY_ATTRIBUTE_START, ENTITY_ATTRIBUTE_END, RESPONSE_IDENTIFIER_DELIMITER, FEATURE_TYPE_SENTENCE, FEATURE_TYPE_SEQUENCE, ACTION_TEXT, ACTION_NAME, TEXT_TOKENS
  from rasa.shared.constants import DIAGNOSTIC_DATA
label_desc: Use image node_id 14 for calling the Message obj's underlying member method code with example usage: obj.get_sparse_features(attribute, featurizers) and returns: combined_sequence_features,  combined_sentence_features