code: def pytorch_model():
      mlflow.pytorch.autolog()
      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)
      return trainer, run
  
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 1 for calling a global function with example usage: pytorch_model() and returns: trainer,  run