code: def test_pytorch_autolog_non_early_stop_callback_does_not_log(
      pytorch_model,
  ):
      trainer, run = pytorch_model
      client = MlflowClient()
      loss_metric_history = client.get_metric_history(
          run.info.run_id, "loss"
      )
      val_loss_metric_history = client.get_metric_history(
          run.info.run_id, "val_loss"
      )
      assert trainer.max_epochs == NUM_EPOCHS
      assert len(loss_metric_history) == NUM_EPOCHS
      assert len(val_loss_metric_history) == NUM_EPOCHS
  
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 20 for calling a global function with example usage: test_pytorch_autolog_non_early_stop_callback_does_not_log(pytorch_model) without return types