code: def test_resolve_intent_response_key_from_label(
      predicted_label: Text,
      train_on_text: bool,
      resolved_intent_response_key: Text,
      response_selector_training_data: TrainingData,
      create_response_selector: Callable[
          [Dict[Text, Any]], ResponseSelector
      ],
  ):
      response_selector = create_response_selector(
          {"use_text_as_label": train_on_text}
      )
      response_selector.preprocess_train_data(
          response_selector_training_data
      )
  
      label_intent_response_key = (
          response_selector._resolve_intent_response_key(
              {"id": hash(predicted_label), "name": predicted_label}
          )
      )
      assert resolved_intent_response_key == label_intent_response_key
      assert (
          response_selector.responses[
              util.intent_response_key_to_template_key(
                  label_intent_response_key
              )
          ]
          == response_selector_training_data.responses[
              util.intent_response_key_to_template_key(
                  resolved_intent_response_key
              )
          ]
      )
  
imports: import copy
  import pytest
  import numpy
  from typing import List, Dict, Text, Any, Optional, Tuple, Union, Callable
  import rasa.model
  from rasa.nlu.featurizers.sparse_featurizer.count_vectors_featurizer import CountVectorsFeaturizer
  from rasa.nlu.featurizers.sparse_featurizer.lexical_syntactic_featurizer import LexicalSyntacticFeaturizer
  from rasa.nlu.featurizers.sparse_featurizer.regex_featurizer import RegexFeaturizer
  from rasa.nlu.tokenizers.whitespace_tokenizer import WhitespaceTokenizer
  from rasa.engine.graph import ExecutionContext, GraphComponent
  from rasa.engine.storage.resource import Resource
  from rasa.engine.storage.storage import ModelStorage
  from rasa.shared.importers.rasa import RasaFileImporter
  from rasa.shared.nlu.training_data import util
  import rasa.shared.nlu.training_data.loading
  from rasa.utils.tensorflow.constants import EPOCHS, MASKED_LM, NUM_TRANSFORMER_LAYERS, RENORMALIZE_CONFIDENCES, TRANSFORMER_SIZE, CONSTRAIN_SIMILARITIES, CHECKPOINT_MODEL, MODEL_CONFIDENCE, RANDOM_SEED, RANKING_LENGTH, LOSS_TYPE, HIDDEN_LAYERS_SIZES, LABEL, EVAL_NUM_EXAMPLES, EVAL_NUM_EPOCHS, RUN_EAGERLY
  from rasa.shared.nlu.constants import TEXT, FEATURE_TYPE_SENTENCE, FEATURE_TYPE_SEQUENCE, INTENT_RESPONSE_KEY, PREDICTED_CONFIDENCE_KEY
  from rasa.utils.tensorflow.model_data_utils import FeatureArray
  from rasa.shared.nlu.training_data.loading import load_data
  from rasa.shared.constants import DIAGNOSTIC_DATA
  from rasa.nlu.selectors.response_selector import ResponseSelector
  from rasa.shared.nlu.training_data.message import Message
  from rasa.shared.nlu.training_data.training_data import TrainingData
  from rasa.nlu.constants import DEFAULT_TRANSFORMER_SIZE, RESPONSE_SELECTOR_PROPERTY_NAME, RESPONSE_SELECTOR_DEFAULT_INTENT, RESPONSE_SELECTOR_PREDICTION_KEY, RESPONSE_SELECTOR_RESPONSES_KEY
label_desc: Use image node_id 9 for calling a global function with example usage: test_resolve_intent_response_key_from_label(predicted_label, train_on_text, resolved_intent_response_key, response_selector_training_data, create_response_selector) without return types