code: def test_train_process_and_load_with_empty_model(
      create_or_load_extractor: Callable[..., RegexEntityExtractor]
  ):
      extractor = create_or_load_extractor({})
      with pytest.warns(UserWarning):
          extractor.train(TrainingData([]))
      with pytest.warns(UserWarning):
          extractor.process(Message(data={TEXT: "arbitrary"}))
      with pytest.warns(UserWarning):
          create_or_load_extractor({}, load=True)
  
file_path: rasa/tests/nlu/extractors/test_regex_entity_extractor.py
imports: import copy
  from rasa.engine.graph import ExecutionContext
  from rasa.engine.storage.storage import ModelStorage
  from typing import Any, Text, Dict, List, Callable
  import pytest
  from rasa.engine.storage.resource import Resource
  from rasa.shared.nlu.training_data.training_data import TrainingData
  from rasa.shared.nlu.training_data.message import Message
  from rasa.shared.nlu.constants import ENTITIES, ENTITY_ATTRIBUTE_TYPE, ENTITY_ATTRIBUTE_VALUE, ENTITY_ATTRIBUTE_START, ENTITY_ATTRIBUTE_END, TEXT, INTENT, EXTRACTOR
  from rasa.nlu.extractors.regex_entity_extractor import RegexEntityExtractor
label_desc: Use image node_id 3 for calling a global function with example usage: test_train_process_and_load_with_empty_model(create_or_load_extractor) without return types