code: def _parse_args():
      # Do we have a config file to parse?
      args_config, remaining = config_parser.parse_known_args()
      if args_config.config:
          with open(args_config.config, "r") as f:
              cfg = yaml.safe_load(f)
              parser.set_defaults(**cfg)
  
      # The main arg parser parses the rest of the args, the usual
      # defaults will have been overridden if config file specified.
      args = parser.parse_args(remaining)
  
      # Cache the args as a text string to save them in the output dir later
      args_text = yaml.safe_dump(
          args.__dict__, default_flow_style=False
      )
      return args, args_text
  
imports: import argparse
  import json
  import logging
  import os
  import time
  from collections import OrderedDict
  from contextlib import suppress
  from datetime import datetime
  from functools import partial
  import torch
  import torch.nn
  import torchvision.utils
  import yaml
  from torch.nn.parallel import DistributedDataParallel
  from timm import utils
  from timm.data import create_dataset, create_loader, resolve_data_config, Mixup, FastCollateMixup, AugMixDataset
  from timm.layers import convert_splitbn_model, convert_sync_batchnorm, set_fast_norm
  from timm.loss import JsdCrossEntropy, SoftTargetCrossEntropy, BinaryCrossEntropy, LabelSmoothingCrossEntropy
  from timm.models import create_model, safe_model_name, resume_checkpoint, load_checkpoint, model_parameters
  from timm.optim import create_optimizer_v2, optimizer_kwargs
  from timm.scheduler import create_scheduler_v2, scheduler_kwargs
  from timm.utils import ApexScaler, NativeScaler
label_desc: Use image node_id 1 for calling a global function with example usage: _parse_args() and returns: args,  args_text