code: def test_pytorch_autolog_raises_error_when_step_logging_unsupported():
      mlflow.pytorch.autolog(log_every_n_step=1)
      model = IrisClassification()
      dm = IrisDataModule()
      trainer = pl.Trainer(max_epochs=NUM_EPOCHS)
      with pytest.raises(
          MlflowException,
          match="log_every_n_step is only supported for PyTorch-Lightning >= 1.1.0",
      ):
          trainer.fit(model, dm)
  
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 12 for calling a global function with example usage: test_pytorch_autolog_raises_error_when_step_logging_unsupported() without return types