code: def add_model_args(parser):
      group = parser.add_argument_group("Model configuration")
      # fmt: off
  
      # Model definitions can be found under fairseq/models/
      #
      # The model architecture can be specified in several ways.
      # In increasing order of priority:
      # 1) model defaults (lowest priority)
      # 2) --arch argument
      # 3) --encoder/decoder-* arguments (highest priority)
      from fairseq.models import ARCH_MODEL_REGISTRY
      group.add_argument('--arch', '-a', metavar='ARCH',
                         choices=ARCH_MODEL_REGISTRY.keys(),
                         help='model architecture')
      # fmt: on
      return group
  
imports: import argparse
  from pathlib import Path
  from typing import Callable, List, Optional, Union
  import torch
  from fairseq import utils
  from fairseq.data.indexed_dataset import get_available_dataset_impl
  from fairseq.dataclass.configs import CheckpointConfig, CommonConfig, CommonEvalConfig, DatasetConfig, DistributedTrainingConfig, EvalLMConfig, GenerationConfig, InteractiveConfig, OptimizationConfig, EMAConfig
  from fairseq.dataclass.utils import gen_parser_from_dataclass
  from fairseq.utils import csv_str_list, eval_bool, eval_str_dict, eval_str_list
label_desc: Use image node_id 20 for calling a global function with example usage: add_model_args(parser) and returns: group