code: def _get_config(
      self,
      max_steps,
      batch_size,
      val_check_interval,
      log_every_n_steps,
      limit_val_batches,
  ):
      config = {
          "trainer": {
              "params": {
                  "max_steps": max_steps,
                  "log_every_n_steps": log_every_n_steps,
                  "val_check_interval": val_check_interval,
                  "limit_val_batches": limit_val_batches,
              }
          },
          "training": {"batch_size": batch_size},
      }
      return get_config_with_defaults(config)
  
imports: import gc
  import unittest
  from typing import Any, Dict, Optional
  from unittest.mock import MagicMock, patch
  from mmf.common.meter import Meter
  from mmf.trainers.callbacks.logistics import LogisticsCallback
  from mmf.trainers.lightning_core.loop_callback import LightningLoopCallback
  from mmf.trainers.lightning_core.loop_callback_with_torchmetrics import LightningTorchMetricsCallback
  from mmf.trainers.lightning_core.torchmetric import LightningTorchMetrics
  from mmf.utils.logger import TensorboardLogger
  from mmf.utils.timer import Timer
  from tests.trainers.test_utils import get_config_with_defaults, get_lightning_trainer, get_mmf_trainer, run_lightning_trainer
label_desc: Use image node_id 7 for calling the TestLightningTrainerValidation obj's underlying member method code with example usage: obj._get_config(max_steps, batch_size, val_check_interval, log_every_n_steps, limit_val_batches) and returns: get_config_with_defaults