code: def _do_a_fp16_pass(self):
      self.trainer.optimizer.zero_grad()
      self.trainer.model.train()
      self.trainer.model.cuda()
      with contextlib.redirect_stdout(StringIO()):
          with torch.cuda.amp.autocast():
              loss = self.trainer.model(
                  torch.rand(5, 5, requires_grad=True).cuda(),
                  torch.empty(5, dtype=torch.long).random_(5).cuda(),
              )
  
      self.trainer.scaler.scale(
          loss["losses"]["total_loss"].sum()
      ).backward()
      self.trainer.scaler.step(self.trainer.optimizer)
      self.trainer.scaler.update()
      self.trainer.lr_scheduler_callback._scheduler.step()
  
imports: import contextlib
  import os
  import tempfile
  import unittest
  from copy import deepcopy
  from io import StringIO
  from unittest.mock import Mock, patch
  import torch
  from mmf.common.registry import registry
  from mmf.models.base_model import BaseModel
  from mmf.trainers.callbacks.checkpoint import CheckpointCallback
  from mmf.trainers.callbacks.early_stopping import EarlyStoppingCallback
  from mmf.trainers.callbacks.lr_scheduler import LRSchedulerCallback
  from mmf.utils.checkpoint import Checkpoint
  from mmf.utils.configuration import load_yaml
  from mmf.utils.file_io import PathManager
  from omegaconf import OmegaConf
  from tests.test_utils import compare_state_dicts, skip_if_no_cuda
label_desc: Use image node_id 13 for calling the TestUtilsCheckpoint obj's underlying member method code with example usage: obj._do_a_fp16_pass() without return types