code: def test_pytorch_autolog_model_can_load_from_artifact(
      pytorch_model_with_callback,
  ):
      _, run = pytorch_model_with_callback
      run_id = run.info.run_id
      client = MlflowClient()
      artifacts = client.list_artifacts(run_id)
      artifacts = (x.path for x in artifacts)
      assert "model" in artifacts
      model = mlflow.pytorch.load_model("runs:/" + run_id + "/model")
      result = model(torch.Tensor([1.5, 2, 2.5, 3.5]).unsqueeze(0))
      assert result is not None
  
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 17 for calling a global function with example usage: test_pytorch_autolog_model_can_load_from_artifact(pytorch_model_with_callback) without return types