class_docstr: Base class to transform the dialogue state into an ML format.
  
  Subclasses of SingleStateFeaturizer will decide how a bot will
  transform the dialogue state into a dictionary mapping an attribute
  to its features. Possible attributes are: `INTENT`, `TEXT`, `ACTION_NAME`,
  `ACTION_TEXT`, `ENTITIES`, `SLOTS` and `ACTIVE_LOOP`. Each attribute will be
  featurized into a list of `rasa.utils.features.Features`.
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code: def _create_features(
      self, sub_state: SubState, attribute: Text, sparse: bool = False
  ) -> List[Features]:
      state_features = self._state_features_for_attribute(
          sub_state, attribute
      )
  
      features = np.zeros(
          len(self._default_feature_states[attribute]), np.float32
      )
      for state_feature, value in state_features.items():
          # check that the value is in default_feature_states to be able to assign
          # its value
          if state_feature in self._default_feature_states[attribute]:
              features[
                  self._default_feature_states[attribute][state_feature]
              ] = value
      features = np.expand_dims(features, 0)
  
      if sparse:
          features = scipy.sparse.coo_matrix(features)
  
      return [
          Features(
              features,
              FEATURE_TYPE_SENTENCE,
              attribute,
              self.__class__.__name__,
          )
      ]
  
imports: import logging
  import numpy
  import scipy.sparse
  from typing import List, Optional, Dict, Text, Set, Any
  from rasa.core.featurizers.precomputation import MessageContainerForCoreFeaturization
  from rasa.nlu.extractors.extractor import EntityTagSpec
  from rasa.nlu.utils import bilou_utils
  from rasa.nlu.utils.bilou_utils import BILOU_PREFIXES
  from rasa.shared.core.domain import SubState, State, Domain
  from rasa.shared.core.constants import PREVIOUS_ACTION, ACTIVE_LOOP, USER, SLOTS
  from rasa.shared.core.trackers import is_prev_action_listen_in_state
  from rasa.shared.nlu.constants import ENTITIES, FEATURE_TYPE_SENTENCE, ACTION_TEXT, ACTION_NAME, INTENT, NO_ENTITY_TAG, ENTITY_ATTRIBUTE_TYPE, ENTITY_TAGS, TEXT
  from rasa.shared.nlu.training_data.features import Features
  from rasa.utils.tensorflow import model_data_utils
label_desc: Use image node_id 5 for calling the SingleStateFeaturizer obj's underlying member method code with example usage: obj._create_features(sub_state, attribute, sparse) and returns: list