code: def test_grad_clipping(self, tmp_path: pathlib.Path) -> None:
      tensorboard_path = tmp_path.joinpath("tensorboard")
  
      training_metrics = {
          "original": [],
          "clipped_by_norm": [],
          "clipped_by_val": [],
      }
      validation_metrics = {
          "original": [],
          "clipped_by_norm": [],
          "clipped_by_val": [],
      }
  
      (
          trial,
          controller,
      ) = pytorch_utils.create_trial_and_trial_controller(
          trial_class=pytorch_onevar_model.OneVarTrialGradClipping,
          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["original"] = metrics_callback.training_metrics
      validation_metrics[
          "original"
      ] = metrics_callback.validation_metrics
  
      updated_hparams = {
          "gradient_clipping_l2_norm": 0.0001,
          **self.hparams,
      }
      (
          trial,
          controller,
      ) = pytorch_utils.create_trial_and_trial_controller(
          trial_class=pytorch_onevar_model.OneVarTrialGradClipping,
          hparams=updated_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[
          "clipped_by_norm"
      ] = metrics_callback.training_metrics
      validation_metrics[
          "clipped_by_norm"
      ] = metrics_callback.validation_metrics
  
      for idx, (original, clipped) in enumerate(
          zip(
              training_metrics["original"],
              training_metrics["clipped_by_norm"],
          )
      ):
          if idx < 10:
              continue
          assert original["loss"] != clipped["loss"]
  
      updated_hparams = {
          "gradient_clipping_value": 0.0001,
          **self.hparams,
      }
      (
          trial,
          controller,
      ) = pytorch_utils.create_trial_and_trial_controller(
          trial_class=pytorch_onevar_model.OneVarTrialGradClipping,
          hparams=updated_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[
          "clipped_by_val"
      ] = metrics_callback.training_metrics
      validation_metrics[
          "clipped_by_val"
      ] = metrics_callback.validation_metrics
  
      for idx, (original, clipped) in enumerate(
          zip(
              training_metrics["original"],
              training_metrics["clipped_by_val"],
          )
      ):
          if idx < 10:
              continue
          assert original["loss"] != clipped["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 10 for calling the TestPyTorchTrial obj's underlying member method code with example usage: obj.test_grad_clipping(tmp_path) without return types