code: def _train_with_condition(
      self,
      num_train_data,
      max_updates,
      max_epochs,
      update_frequency,
      batch_size,
      on_update_end_fn=None,
  ):
      torch.random.manual_seed(2)
      config = self._get_config(
          max_updates=max_updates,
          max_epochs=max_epochs,
          update_frequency=update_frequency,
          batch_size=batch_size,
      )
      trainer = get_mmf_trainer(
          num_data_size=num_train_data, config=config
      )
      if on_update_end_fn:
          trainer.on_update_end = on_update_end_fn
      trainer.training_loop()
      return trainer
  
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 6 for calling the TestTrainingLoop obj's underlying member method code with example usage: obj._train_with_condition(num_train_data, max_updates, max_epochs, update_frequency, batch_size, on_update_end_fn) and returns: trainer