code: def get_conversion_arguments(args: Optional[List[str]] = None):
      parser = get_training_arguments(parse_args=False)
  
      # Arguments related to coreml conversion
      group = parser.add_argument_group("Conversion arguments")
      group.add_argument(
          "--conversion.coreml-extn",
          type=str,
          default="mlmodel",
          help="Extension for converted model. Default is mlmodel",
      )
      group.add_argument(
          "--conversion.input-image-path",
          type=str,
          default=None,
          help="Path of the image to be used for conversion",
      )
  
      # Arguments related to server.
      group.add_argument(
          "--conversion.bucket-name",
          type=str,
          help="Model job's bucket name",
      )
      group.add_argument(
          "--conversion.task-id", type=str, help="Model job's id"
      )
      group.add_argument(
          "--conversion.viewers",
          type=str,
          nargs="+",
          default=None,
          help="Users who can view your models on server",
      )
  
      # 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 6 for calling a global function with example usage: get_conversion_arguments(args) and returns: parser_to_opts