code: def pytorch_model_with_callback(patience):
      mlflow.pytorch.autolog()
      model = IrisClassification()
      dm = IrisDataModule()
      dm.setup(stage="fit")
      early_stopping = EarlyStopping(
          monitor="val_loss",
          mode="min",
          min_delta=99999999,  # forces early stopping
          patience=patience,
          verbose=True,
      )
  
      with TempDir() as tmp:
          keyword = (
              "dirpath"
              if Version(pl.__version__) >= Version("1.2.0")
              else "filepath"
          )
          checkpoint_callback = ModelCheckpoint(
              **{keyword: tmp.path()},
              save_top_k=1,
              verbose=True,
              monitor="val_loss",
              mode="min",
          )
  
          trainer = pl.Trainer(
              max_epochs=NUM_EPOCHS * 2,
              callbacks=[early_stopping, checkpoint_callback],
          )
          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 15 for calling a global function with example usage: pytorch_model_with_callback(patience) and returns: trainer,  run