code: def test_create_features__dtype_float():
      f = SingleStateFeaturizer()
      f._default_feature_states[INTENT] = {"a": 0, "b": 1}
      f._default_feature_states[ACTION_NAME] = {"e": 0, "d": 1}
      f._default_feature_states[ENTITIES] = {"c": 0}
  
      encoded = f._create_features(
          {ACTION_NAME: "d"}, attribute=ACTION_NAME
      )
      assert len(encoded) == 1  # cause for some reason this is a list
      assert encoded[0].features.dtype == np.float32
  
imports: from typing import Text
  import numpy
  import re
  import scipy.sparse
  import pytest
  from rasa.nlu.tokenizers.tokenizer import Token
  from rasa.nlu.constants import TOKENS_NAMES
  from rasa.core.featurizers.precomputation import MessageContainerForCoreFeaturization
  from rasa.core.featurizers.single_state_featurizer import SingleStateFeaturizer
  from rasa.shared.nlu.training_data.features import Features
  from rasa.shared.nlu.training_data.message import Message
  from rasa.shared.nlu.constants import ACTION_TEXT, ACTION_NAME, ENTITIES, TEXT, INTENT, FEATURE_TYPE_SEQUENCE, FEATURE_TYPE_SENTENCE, ENTITY_ATTRIBUTE_TYPE, ENTITY_ATTRIBUTE_VALUE, ENTITY_ATTRIBUTE_START, ENTITY_ATTRIBUTE_END, ENTITY_TAGS
  from rasa.shared.core.domain import Domain
  from rasa.shared.core.slots import TextSlot
  from rasa.shared.core.constants import ACTION_LISTEN_NAME, ACTIVE_LOOP, PREVIOUS_ACTION, SLOTS, ENTITY_LABEL_SEPARATOR, USER
  from rasa.utils.tensorflow.constants import SENTENCE, SEQUENCE
label_desc: Use image node_id 3 for calling a global function with example usage: test_create_features__dtype_float() without return types