code: def _do_a_pass(self):
      self.trainer.optimizer.zero_grad()
      self.trainer.model.train()
      with contextlib.redirect_stdout(StringIO()):
          loss = self.trainer.model(
              torch.rand(5, 5, requires_grad=True),
              torch.empty(5, dtype=torch.long).random_(5),
          )
  
      loss["losses"]["total_loss"].sum().backward()
      self.trainer.optimizer.step()
      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 12 for calling the TestUtilsCheckpoint obj's underlying member method code with example usage: obj._do_a_pass() without return types