code: def test_convert_tags_to_entities(
      text: Text,
      tags: Dict[Text, List[Text]],
      confidences: Dict[Text, List[float]],
      expected_entities: List[Dict[Text, Any]],
      whitespace_tokenizer: WhitespaceTokenizer,
  ):
      extractor = EntityExtractorMixin()
  
      message = Message(data={TEXT: text})
      tokens = whitespace_tokenizer.tokenize(message, TEXT)
  
      split_entities_config = {SPLIT_ENTITIES_BY_COMMA: True}
      actual_entities = extractor.convert_predictions_into_entities(
          text, tokens, tags, split_entities_config, confidences
      )
      assert actual_entities == expected_entities
  
file_path: rasa/tests/nlu/extractors/test_extractor.py
imports: from typing import Any, Text, Dict, List
  import pytest
  from rasa.shared.constants import LATEST_TRAINING_DATA_FORMAT_VERSION
  from rasa.shared.nlu.constants import TEXT, SPLIT_ENTITIES_BY_COMMA
  from rasa.shared.nlu.training_data.message import Message
  from rasa.nlu.extractors.extractor import EntityExtractorMixin
  from rasa.nlu.tokenizers.whitespace_tokenizer import WhitespaceTokenizer
  from rasa.shared.nlu.training_data.formats.rasa_yaml import RasaYAMLReader
label_desc: Use image node_id 1 for calling a global function with example usage: test_convert_tags_to_entities(text, tags, confidences, expected_entities, whitespace_tokenizer) without return types