code: def test_grad_clipping_and_parity_to_mmf(self):
      config = self._get_mmf_config(
          max_updates=5,
          max_epochs=None,
          max_grad_l2_norm=self.grad_clip_magnitude,
          clip_norm_mode="all",
      )
      mmf_trainer = get_mmf_trainer(config=config)
      mmf_trainer.evaluation_loop = MagicMock(return_value=(None, None))
  
      def _finish_update():
          clip_gradients(
              mmf_trainer.model,
              mmf_trainer.optimizer,
              mmf_trainer.num_updates,
              None,
              mmf_trainer.config,
          )
          for param in mmf_trainer.model.parameters():
              mmf_grad = torch.clone(param.grad).detach().item()
              self.mmf_grads.append(mmf_grad)
  
          mmf_trainer.scaler.step(mmf_trainer.optimizer)
          mmf_trainer.scaler.update()
          mmf_trainer.num_updates += 1
  
      mmf_trainer._finish_update = _finish_update
      mmf_trainer.training_loop()
  
      with patch(
          "mmf.trainers.lightning_trainer.get_mmf_env", return_value=""
      ):
          config = self._get_config(
              max_steps=5,
              max_epochs=None,
              gradient_clip_val=self.grad_clip_magnitude,
          )
          trainer = get_lightning_trainer(config=config)
          trainer.callbacks.append(self)
          trainer.trainer.fit(
              trainer.model, trainer.data_module.train_loader
          )
  
file_path: mmf/tests/trainers/lightning/test_grad_clipping.py
imports: import unittest
  from unittest.mock import MagicMock, patch
  import torch
  from mmf.utils.general import clip_gradients
  from pytorch_lightning.callbacks.base import Callback
  from tests.trainers.test_utils import get_config_with_defaults, get_lightning_trainer, get_mmf_trainer
label_desc: Use image node_id 2 for calling the TestLightningTrainerGradClipping obj's underlying member method code with example usage: obj.test_grad_clipping_and_parity_to_mmf() without return types