code: def test_gradient_aggregation(self, tmp_path: pathlib.Path) -> None:
      tensorboard_path = tmp_path.joinpath("tensorboard")
  
      AGG_FREQ = 2
      exp_config = utils.make_default_exp_config(
          self.hparams,
          scheduling_unit=1,
          searcher_metric=pytorch_onevar_model.OneVarTrial._searcher_metric,
      )
      exp_config["optimizations"].update(
          {
              "aggregation_frequency": AGG_FREQ,
              "average_aggregated_gradients": True,
          }
      )
  
      (
          trial,
          controller,
      ) = pytorch_utils.create_trial_and_trial_controller(
          exp_config=exp_config,
          trial_class=pytorch_onevar_model.OneVarTrial,
          hparams=self.hparams,
          trial_seed=self.trial_seed,
          max_batches=100,
          min_validation_batches=10,
          min_checkpoint_batches=sys.maxsize,
          tensorboard_path=tensorboard_path,
      )
  
      controller.run()
  
      metrics_callback = trial.metrics_callback
      training_metrics = metrics_callback.training_metrics
      # Check the gradient update at every step.
      for idx, batch_metrics in enumerate(training_metrics):
          if (idx + 1) % AGG_FREQ != 0:
              # Only test batches which land on aggregation_frequency boundaries.
              continue
          pytorch_onevar_model.OneVarTrial.check_batch_metrics(
              batch_metrics,
              idx,
              metric_keyname_pairs=(
                  ("loss", "loss_exp"),
                  ("w_after", "w_exp"),
              ),
          )
  
      for older, newer in zip(training_metrics, training_metrics[1:]):
          assert newer["loss"] <= older["loss"]
  
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 20 for calling the TestPyTorchTrial obj's underlying member method code with example usage: obj.test_gradient_aggregation(tmp_path) without return types