code: def test_trial_validation_checkpointing(self, tmp_path: pathlib.Path):
      tensorboard_path = tmp_path.joinpath("tensorboard")
  
      (
          trial,
          controller,
      ) = pytorch_utils.create_trial_and_trial_controller(
          trial_class=pytorch_onevar_model.OneVarTrial,
          hparams=self.hparams,
          trial_seed=self.trial_seed,
          max_batches=100,
          tensorboard_path=tensorboard_path,
      )
  
      # Checkpoint only if the following conditions are met:
      # - the checkpoint is not current
      # - the best validation metric returned is better per smaller_is_better
      checkpoint_conditions = [
          {
              "checkpoint_is_current": False,
              "best_validation": float("inf"),
              "smaller_is_better": True,
              "checkpoint": True,
          },
          {
              "checkpoint_is_current": False,
              "best_validation": float("-inf"),
              "smaller_is_better": False,
              "checkpoint": True,
          },
          {
              "checkpoint_is_current": False,
              "best_validation": sys.maxsize,
              "smaller_is_better": False,
              "checkpoint": False,
          },
          {
              "checkpoint_is_current": True,
              "best_validation": sys.maxsize,
              "smaller_is_better": False,
              "checkpoint": False,
          },
      ]
      for checkpoint_condition in checkpoint_conditions:
          controller.smaller_is_better = checkpoint_condition[
              "smaller_is_better"
          ]
          controller._checkpoint_is_current = mock.MagicMock(
              return_value=checkpoint_condition["checkpoint_is_current"]
          )
          controller.core_context.train.get_experiment_best_validation = mock.MagicMock(
              return_value=checkpoint_condition["best_validation"]
          )
          controller._checkpoint = mock.MagicMock()
          controller._validate(
              det.core.DummySearcherOperation(length=100, is_chief=True)
          )
          controller.core_context.train.get_experiment_best_validation.assert_called_once()
          if checkpoint_condition["checkpoint"]:
              controller._checkpoint.assert_called_once()
          controller.core_context.train.get_experiment_best_validation.reset_mock()
          controller._checkpoint.reset_mock()
  
imports: import contextlib
  import importlib
  import io
  import os
  import pathlib
  import sys
  import typing
  from unittest import mock
  import numpy
  import pytest
  import torch
  from _pytest import monkeypatch
  from torch.distributed import launcher
  import determined
  from determined import pytorch
  from tests.experiment import pytorch_utils, utils
  from tests.experiment.fixtures import pytorch_onevar_model
label_desc: Use image node_id 33 for calling the TestPyTorchTrial obj's underlying member method code with example usage: obj.test_trial_validation_checkpointing(tmp_path) without return types