code: def test_train_selector(
      response_selector_training_data: TrainingData,
      config_params: Dict[Text, Any],
      create_response_selector: Callable[
          [Dict[Text, Any]], ResponseSelector
      ],
      default_model_storage: ModelStorage,
      train_and_preprocess: Callable[
          ..., Tuple[TrainingData, List[GraphComponent]]
      ],
      process_message: Callable[..., Message],
  ):
      pipeline = [
          {"component": WhitespaceTokenizer},
          {"component": CountVectorsFeaturizer},
      ]
      (
          response_selector_training_data,
          loaded_pipeline,
      ) = train_and_preprocess(
          pipeline, response_selector_training_data
      )
  
      response_selector = create_response_selector(config_params)
      response_selector.train(
          training_data=response_selector_training_data
      )
  
      message = Message(data={TEXT: "hello"})
      message = process_message(loaded_pipeline, message)
  
      classified_message = response_selector.process([message])[0]
  
      assert classified_message is not None
      assert (
          classified_message.get("response_selector").get(
              "all_retrieval_intents"
          )
      ) == ["chitchat"]
      assert (
          classified_message.get("response_selector")
          .get("default")
          .get("response")
          .get("intent_response_key")
      ) is not None
      assert (
          classified_message.get("response_selector")
          .get("default")
          .get("response")
          .get("utter_action")
      ) is not None
      assert (
          classified_message.get("response_selector")
          .get("default")
          .get("response")
          .get("responses")
      ) is not None
  
      ranking = (
          classified_message.get("response_selector")
          .get("default")
          .get("ranking")
      )
      assert ranking is not None
  
      for rank in ranking:
          assert rank.get("confidence") is not None
          assert rank.get("intent_response_key") is not None
  
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 6 for calling a global function with example usage: test_train_selector(response_selector_training_data, config_params, create_response_selector, default_model_storage, train_and_preprocess, process_message) without return types