code: def main():
      args = parse_args()
  
      # Reduce the number of repeated compilations and improve
      # training speed.
      setup_cache_size_limit_of_dynamo()
  
      # load config
      cfg = Config.fromfile(args.config)
      cfg.launcher = args.launcher
      if args.cfg_options is not None:
          cfg.merge_from_dict(args.cfg_options)
  
      # work_dir is determined in this priority: CLI > segment in file > filename
      if args.work_dir is not None:
          # update configs according to CLI args if args.work_dir is not None
          cfg.work_dir = args.work_dir
      elif cfg.get("work_dir", None) is None:
          # use config filename as default work_dir if cfg.work_dir is None
          cfg.work_dir = osp.join(
              "./work_dirs", osp.splitext(osp.basename(args.config))[0]
          )
  
      # enable automatic-mixed-precision training
      if args.amp is True:
          cfg.optim_wrapper.type = "AmpOptimWrapper"
          cfg.optim_wrapper.loss_scale = "dynamic"
  
      # enable automatically scaling LR
      if args.auto_scale_lr:
          if (
              "auto_scale_lr" in cfg
              and "enable" in cfg.auto_scale_lr
              and "base_batch_size" in cfg.auto_scale_lr
          ):
              cfg.auto_scale_lr.enable = True
          else:
              raise RuntimeError(
                  'Can not find "auto_scale_lr" or '
                  '"auto_scale_lr.enable" or '
                  '"auto_scale_lr.base_batch_size" in your'
                  " configuration file."
              )
  
      # resume is determined in this priority: resume from > auto_resume
      if args.resume == "auto":
          cfg.resume = True
          cfg.load_from = None
      elif args.resume is not None:
          cfg.resume = True
          cfg.load_from = args.resume
  
      # build the runner from config
      if "runner_type" not in cfg:
          # build the default runner
          runner = Runner.from_cfg(cfg)
      else:
          # build customized runner from the registry
          # if 'runner_type' is set in the cfg
          runner = RUNNERS.build(cfg)
  
      # start training
      runner.train()
  
file_path: mmdetection/tools/train.py
imports: import argparse
  import os
  import os.path
  from mmengine.config import Config, DictAction
  from mmengine.registry import RUNNERS
  from mmengine.runner import Runner
  from mmdet.utils import setup_cache_size_limit_of_dynamo
label_desc: Use image node_id 2 for calling a global function with example usage: main() without return types