code: def test_encode_all_labels__encoded_all_action_names_and_texts():
      # ... where "labels" means actions...
      domain = Domain(
          intents=[],
          entities=[],
          slots=[],
          responses={},
          forms={},
          action_names=["a", "b", "c", "d"],
          data={},
      )
  
      f = SingleStateFeaturizer()
      f.prepare_for_training(domain)
  
      precomputations = MessageContainerForCoreFeaturization()
      precomputations.derive_messages_from_domain_and_add(domain)
  
      encoded_actions = f.encode_all_labels(
          domain, precomputations=precomputations
      )
  
      assert len(encoded_actions) == len(domain.action_names_or_texts)
      assert all(
          [
              ACTION_NAME in encoded_action
              and ACTION_TEXT not in encoded_action
              for encoded_action in encoded_actions
          ]
      )
  
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 5 for calling a global function with example usage: test_encode_all_labels__encoded_all_action_names_and_texts() without return types