code: def response_selector_training_data() -> TrainingData:
      # use data that include some responses
      training_data = rasa.shared.nlu.training_data.loading.load_data(
          "data/examples/rasa/demo-rasa.yml"
      )
      training_data_responses = (
          rasa.shared.nlu.training_data.loading.load_data(
              "data/examples/rasa/demo-rasa-responses.yml"
          )
      )
      training_data = training_data.merge(training_data_responses)
  
      return training_data
  
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 1 for calling a global function with example usage: response_selector_training_data() and returns: training_data