code: def test_pytorch_autolog_logs_expected_metrics_without_validation(
      pytorch_model_without_validation,
  ):
      trainer, run = pytorch_model_without_validation
      assert not trainer.enable_validation
  
      client = MlflowClient()
      for metric_key in ["loss", "train_acc"]:
          assert metric_key in run.data.metrics
          metric_history = client.get_metric_history(
              run.info.run_id, metric_key
          )
          assert len(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 9 for calling a global function with example usage: test_pytorch_autolog_logs_expected_metrics_without_validation(pytorch_model_without_validation) without return types