code: def test_pytorch_autolog_log_models_configuration(log_models):
      mlflow.pytorch.autolog(log_models=log_models)
      model = IrisClassification()
      dm = IrisDataModule()
      dm.setup(stage="fit")
      trainer = pl.Trainer(max_epochs=NUM_EPOCHS)
      trainer.fit(model, dm)
      client = MlflowClient()
      run = client.get_run(client.search_runs(["0"])[0].info.run_id)
      run_id = run.info.run_id
      client = MlflowClient()
      artifacts = [f.path for f in client.list_artifacts(run_id)]
      assert ("model" in artifacts) == log_models
  
imports: import pytest
  import pytorch_lightning
  import torch
  from iris import IrisClassification, IrisClassificationMultiOptimizer, IrisClassificationWithoutValidation
  from iris_data_module import IrisDataModule, IrisDataModuleWithoutValidation
  from packaging.version import Version
  from pytorch_lightning.callbacks import ModelCheckpoint
  from pytorch_lightning.callbacks.early_stopping import EarlyStopping
  import mlflow
  import mlflow.pytorch
  from mlflow import MlflowClient
  from mlflow.exceptions import MlflowException
  from mlflow.pytorch._lightning_autolog import _get_optimizer_name
  from mlflow.utils.file_utils import TempDir
label_desc: Use image node_id 5 for calling a global function with example usage: test_pytorch_autolog_log_models_configuration(log_models) without return types