code: def test_nemo_checkpoint_save_best_model_1(self, tmp_path):
      test_trainer = pl.Trainer(
          accelerator="cpu",
          enable_checkpointing=False,
          logger=False,
          max_epochs=4,
      )
      exp_manager(
          test_trainer,
          {
              "checkpoint_callback_params": {"save_best_model": True},
              "explicit_log_dir": str(tmp_path / "test"),
          },
      )
      model = ExampleModel()
      test_trainer.fit(model)
  
      assert Path(
          str(tmp_path / "test" / "checkpoints" / "default.nemo")
      ).exists()
  
      model = ExampleModel.restore_from(
          str(tmp_path / "test" / "checkpoints" / "default.nemo")
      )
      assert (
          float(model(torch.tensor([1.0, 1.0], device=model.device)))
          == 0.0
      )
  
imports: import math
  import os
  import re
  from pathlib import Path
  from typing import Any
  import pytest
  import pytorch_lightning
  import torch
  from omegaconf import OmegaConf
  from omegaconf.errors import OmegaConfBaseException
  from pytorch_lightning import Callback
  from pytorch_lightning.loops import _TrainingEpochLoop
  from nemo.constants import NEMO_ENV_VARNAME_VERSION
  from nemo.core.classes import ModelPT
  from nemo.utils.exp_manager import CheckpointMisconfigurationError, LoggerMisconfigurationError, NotFoundError, exp_manager
label_desc: Use image node_id 7 for calling the TestExpManager obj's underlying member method code with example usage: obj.test_nemo_checkpoint_save_best_model_1(tmp_path) without return types