code: def run(
      opts: typing.Optional[typing.List[str]] = None,
      predict: bool = False,
  ):
      """Run starts a job based on the command passed from the command line.
      You can optionally run the mmf job programmatically by passing an optlist as opts.
  
      Args:
          opts (typing.Optional[typing.List[str]], optional): Optlist which can be used.
              to override opts programmatically. For e.g. if you pass
              opts = ["training.batch_size=64", "checkpoint.resume=True"], this will
              set the batch size to 64 and resume from the checkpoint if present.
              Defaults to None.
          predict (bool, optional): If predict is passed True, then the program runs in
              prediction mode. Defaults to False.
      """
      setup_imports()
  
      if opts is None:
          parser = flags.get_parser()
          args = parser.parse_args()
      else:
          args = argparse.Namespace(config_override=None)
          args.opts = opts
  
      configuration = Configuration(args)
      # Do set runtime args which can be changed by MMF
      configuration.args = args
      config = configuration.get_config()
      config.start_rank = 0
      if config.distributed.init_method is None:
          infer_init_method(config)
  
      if config.distributed.init_method is not None:
          if (
              torch.cuda.device_count() > 1
              and not config.distributed.no_spawn
          ):
              config.start_rank = config.distributed.rank
              config.distributed.rank = None
              torch.multiprocessing.spawn(
                  fn=distributed_main,
                  args=(configuration, predict),
                  nprocs=torch.cuda.device_count(),
              )
          else:
              distributed_main(0, configuration, predict)
      elif config.distributed.world_size > 1:
          if is_xla():
              import torch_xla.distributed.xla_multiprocessing as xmp
  
              torch.multiprocessing.set_sharing_strategy("file_system")
              xmp.spawn(
                  fn=distributed_main,
                  args=(configuration, predict),
                  nprocs=8,  # use all 8 TPU cores
                  start_method="fork",
              )
          else:
              assert (
                  config.distributed.world_size
                  <= torch.cuda.device_count()
              )
              port = random.randint(10000, 20000)
              config.distributed.init_method = f"tcp://localhost:{port}"
              config.distributed.rank = None
              torch.multiprocessing.spawn(
                  fn=distributed_main,
                  args=(configuration, predict),
                  nprocs=config.distributed.world_size,
              )
      else:
          config.device_id = 0
          main(configuration, predict=predict)
  
file_path: mmf/mmf_cli/run.py
imports: import argparse
  import logging
  import random
  import typing
  import torch
  from mmf.common.registry import registry
  from mmf.utils.build import build_config, build_trainer
  from mmf.utils.configuration import Configuration
  from mmf.utils.distributed import distributed_init, get_rank, infer_init_method, is_xla
  from mmf.utils.env import set_seed, setup_imports
  from mmf.utils.flags import flags
  from mmf.utils.general import log_device_names
  from mmf.utils.logger import setup_logger, setup_very_basic_config
label_desc: Use image node_id 3 for calling a global function with example usage: run(opts, predict) without return types