code: def test_autolog_registering_model():
      registered_model_name = "test_autolog_registered_model"
      mlflow.pytorch.autolog(
          registered_model_name=registered_model_name
      )
      model = IrisClassification()
      dm = IrisDataModule()
      dm.setup(stage="fit")
      trainer = pl.Trainer(max_epochs=NUM_EPOCHS)
  
      with mlflow.start_run():
          trainer.fit(model, dm)
  
          registered_model = MlflowClient().get_registered_model(
              registered_model_name
          )
          assert registered_model.name == registered_model_name
  
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 26 for calling a global function with example usage: test_autolog_registering_model() without return types