code: def test_train_with_only_nlu_data(
      run_in_simple_project: Callable[..., RunResult]
  ):
      temp_dir = Path.cwd()
  
      for core_file in ["stories.yml", "rules.yml"]:
          assert (temp_dir / "data" / core_file).exists()
          (temp_dir / "data" / core_file).unlink()
  
      run_in_simple_project("train", "--fixed-model-name", "test-model")
  
      assert os.path.exists(os.path.join(temp_dir, "models"))
      files = rasa.shared.utils.io.list_files(
          os.path.join(temp_dir, "models")
      )
      assert len(files) == 1
      assert os.path.basename(files[0]) == "test-model.tar.gz"
  
imports: import os
  import sys
  from pathlib import Path
  from _pytest.capture import CaptureFixture
  import pytest
  from typing import Callable, List
  from _pytest.pytester import RunResult
  from _pytest.tmpdir import TempPathFactory
  import rasa.shared.utils.io
  from rasa.constants import NUMBER_OF_TRAINING_STORIES_FILE
  from rasa.core.policies.policy import Policy
  from rasa.engine.storage.local_model_storage import LocalModelStorage
  from rasa.engine.storage.resource import Resource
  from rasa.shared.core.domain import Domain
  from rasa.model_training import CODE_NEEDS_TO_BE_RETRAINED, CODE_FORCED_TRAINING
  from rasa.shared.constants import LATEST_TRAINING_DATA_FORMAT_VERSION
  from rasa.shared.nlu.training_data.training_data import DEFAULT_TRAINING_DATA_OUTPUT_PATH
  import rasa.utils.io
  from tests.cli.conftest import RASA_EXE
label_desc: Use image node_id 10 for calling a global function with example usage: test_train_with_only_nlu_data(run_in_simple_project) without return types