code: def test_update_frequency_reporting(self, a):
      def _on_update_end(report, meter, should_log):
          # the losses here should be the sum of two losses in
          # iteration 0 and iteration 1 (both constitute update 0).
          # Here iter 1 loss: 0.2599, iter 2 loss: 4.2090
          loss = report.losses["loss"].detach().cpu().item()
          self.assertAlmostEqual(loss, 4.4688, 4)
  
      self._train_with_condition(
          num_train_data=100,
          max_updates=1,
          max_epochs=None,
          update_frequency=2,
          batch_size=2,
          on_update_end_fn=_on_update_end,
      )
  
imports: import unittest
  from unittest.mock import MagicMock, patch
  import torch
  from mmf.common.registry import registry
  from mmf.utils.general import get_batch_size
  from tests.test_utils import SimpleModel, SimpleNaNLossModel
  from tests.trainers.test_trainer_mocks import TrainerTrainingLoopMock
  from tests.trainers.test_utils import add_model, add_optimizer, get_config_with_defaults, get_mmf_trainer
label_desc: Use image node_id 2 for calling the TestTrainingLoop obj's underlying member method code with example usage: obj.test_update_frequency_reporting(a) without return types