code: def test_training_metrics(self, tmp_path: pathlib.Path) -> None:
      tensorboard_path = tmp_path.joinpath("tensorboard")
  
      (
          trial,
          trial_controller,
      ) = pytorch_utils.create_trial_and_trial_controller(
          trial_class=pytorch_onevar_model.OneVarTrialWithTrainingMetrics,
          hparams=self.hparams,
          trial_seed=self.trial_seed,
          tensorboard_path=tensorboard_path,
      )
  
      train_steps, metrics = trial_controller._train_with_boundaries(
          training_enumerator=enumerate(
              trial_controller.training_iterator
          ),
          train_boundaries=[
              pytorch._TrainBoundary(
                  step_type=pytorch._TrainBoundaryType.TRAIN,
                  unit=pytorch.Batch(100),
              )
          ],
      )
  
      assert len(train_steps) == 1, "unexpected train step count"
      assert train_steps[
          0
      ].limit_reached, "train step did not reach expected limit"
      assert len(metrics) == 100, "metrics length did not match input"
      for metric in metrics:
          assert "mse" in metric
  
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 3 for calling the TestPyTorchTrial obj's underlying member method code with example usage: obj.test_training_metrics(tmp_path) without return types