code: def test_clip_grad_norm(self):
      torch.manual_seed(self.seed + self.rank)
  
      # Identical models with data-parallel and ZeRO
      ref_model, ref_optim, dist_model, dist_optim = make_models(1, 1)
  
      # Training steps with pre-determined gradients
      xs = [3, 1, 4, 1, 5, 9]
      dys = [1, -1, 1, -1, 1, -1]
      for x, dy in zip(xs, dys):
          x = torch.tensor([[x]], dtype=torch.float32, device="cuda")
          dy = torch.tensor([[dy]], dtype=torch.float32, device="cuda")
  
          # Reference implementation
          ref_optim.zero_grad()
          y_ref = ref_model(x.detach())
          y_ref.backward(dy.detach())
          ref_grad_norm = torch.nn.utils.clip_grad_norm_(
              ref_model.parameters(), 3.5
          )
          ref_optim.step()
  
          # Distributed implementation
          dist_optim.zero_grad()
          y_dist = dist_model(x.detach())
          y_dist.backward(dy.detach())
          dist_grad_norm = dist_optim.clip_grad_norm(3.5)
          dist_optim.step()
  
          # Check that parameters match
          torch.testing.assert_close(dist_grad_norm, ref_grad_norm)
          for ref_param, dist_param in zip(
              ref_model.parameters(), dist_model.parameters()
          ):
              torch.testing.assert_close(dist_param, ref_param)
  
imports: from contextlib import contextmanager
  import io
  from typing import Callable, Optional, Tuple
  import unittest
  import warnings
  import torch
  from torch.testing._internal import common_utils
  from apex.transformer.testing.distributed_test_base import NcclDistributedTestBase
label_desc: Use image node_id 17 for calling the TestDistributedFusedAdam obj's underlying member method code with example usage: obj.test_clip_grad_norm() without return types