code: def get_benchmarking_arguments(args: Optional[List[str]] = None):
      parser = get_training_arguments(parse_args=False)
  
      #
      group = parser.add_argument_group("Benchmarking arguments")
      group.add_argument(
          "--benchmark.batch-size",
          type=int,
          default=1,
          help="Batch size for benchmarking",
      )
      group.add_argument(
          "--benchmark.warmup-iter",
          type=int,
          default=10,
          help="Warm-up iterations",
      )
      group.add_argument(
          "--benchmark.n-iter",
          type=int,
          default=100,
          help="Number of iterations for benchmarking",
      )
      group.add_argument(
          "--benchmark.use-jit-model",
          action="store_true",
          help="Convert the model to JIT and then benchmark it",
      )
  
      # parse args
      return parser_to_opts(parser, args=args)
  
imports: import argparse
  from typing import List, Optional
  from common import SUPPORTED_MODALITIES
  from cvnets import modeling_arguments
  from data.collate_fns import arguments_collate_fn
  from data.datasets import arguments_dataset
  from data.sampler import add_sampler_arguments
  from data.text_tokenizer import arguments_tokenizer
  from data.transforms import arguments_augmentation
  from data.video_reader import arguments_video_reader
  from loss_fn import add_loss_fn_arguments
  from metrics import METRICS_REGISTRY, arguments_stats
  from optim import arguments_optimizer
  from optim.scheduler import arguments_scheduler
  from options.utils import load_config_file
  from utils import logger
label_desc: Use image node_id 7 for calling a global function with example usage: get_benchmarking_arguments(args) and returns: parser_to_opts