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`).
code: def _filter_dense_features(
      self, attribute: Text, featurizers: List[Text]
  ) -> Tuple[List["Features"], List["Features"]]:
      sentence_features = [
          f
          for f in self.features
          if f.attribute == attribute
          and f.is_dense()
          and f.type == FEATURE_TYPE_SENTENCE
          and (f.origin in featurizers or not featurizers)
      ]
      sequence_features = [
          f
          for f in self.features
          if f.attribute == attribute
          and f.is_dense()
          and f.type == FEATURE_TYPE_SEQUENCE
          and (f.origin in featurizers or not featurizers)
      ]
      return sequence_features, 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 19 for calling the Message obj's underlying member method code with example usage: obj._filter_dense_features(attribute, featurizers) and returns: sequence_features,  sentence_features