code: def _update_specific(self, config):
      # tp = self.config.training
  
      # if args["seed"] is not None or tp['seed'] is not None:
      #     print(
      #         "You have chosen to seed the training. This will turn on CUDNN "
      #         "deterministic setting which can slow down your training "
      #         "considerably! You may see unexpected behavior when restarting "
      #         "from checkpoints."
      #     )
  
      # if args["seed"] == -1:
      #     self.config["training"]["seed"] = random.randint(1, 1000000)
  
      if (
          "learning_rate" in config
          and "optimizer" in config
          and "params" in config.optimizer
      ):
          lr = config.learning_rate
          config.optimizer.params.lr = lr
  
      # TODO: Correct the following issue
      # This check is triggered before the config override from
      # commandline is effective even after setting
      # training.device = 'xla', it gets triggered.
      if (
          not torch.cuda.is_available()
          and "cuda" in config.training.device
      ):
          warnings.warn(
              "Device specified is 'cuda' but cuda is not present. "
              + "Switching to CPU version."
          )
          config.training.device = "cpu"
  
      return config
  
imports: import collections
  import json
  import logging
  import os
  import warnings
  from ast import literal_eval
  from typing import List
  import torch
  from mmf.common.registry import registry
  from mmf.utils.env import import_user_module
  from mmf.utils.file_io import PathManager
  from mmf.utils.general import get_absolute_path, get_mmf_root
  from omegaconf import DictConfig, errors, OmegaConf
label_desc: Use image node_id 18 for calling the Configuration obj's underlying member method code with example usage: obj._update_specific(config) and returns: config