class_docstr: Reader for NLU training data.
code: def _read_entities(
      entity: Dict[Text, Any], examples: List[Dict[Text, Any]]
  ) -> "TrainingData":
      entity_synonyms = transform_entity_synonyms(examples)
  
      if entity["isRegexp"]:
          regex_features = DialogflowReader._extract_regex_features(
              entity, examples
          )
          return TrainingData([], entity_synonyms, regex_features, [])
      else:
          lookup_tables = DialogflowReader._extract_lookup_tables(
              entity, examples
          )
          return TrainingData([], entity_synonyms, [], lookup_tables)
  
imports: import logging
  import os
  from pathlib import Path
  from typing import Any, Dict, Optional, Text, List, Tuple, Union
  import rasa.shared.nlu.training_data.util
  from rasa.shared.constants import DOCS_BASE_URL
  from rasa.shared.nlu.training_data.formats.readerwriter import TrainingDataReader
  from rasa.shared.nlu.training_data.util import transform_entity_synonyms
  import rasa.shared.utils.io
  from rasa.shared.nlu.training_data.training_data import TrainingData
  from rasa.shared.nlu.training_data.message import Message
label_desc: Use image node_id 8 for calling the DialogflowReader obj's underlying member method code with example usage: obj._read_entities(entity, examples) and returns: TrainingData, TrainingData