code: def distributed_worker(i, main, opts, kwargs):
      setattr(opts, "dev.device_id", i)
      torch.cuda.set_device(i)
      setattr(opts, "dev.device", torch.device(f"cuda:{i}"))
  
      ddp_rank = getattr(opts, "ddp.rank", None)
      if ddp_rank is None:  # torch.multiprocessing.spawn
          ddp_rank = kwargs.get("start_rank", 0) + i
          setattr(opts, "ddp.rank", ddp_rank)
  
      node_rank = distributed_init(opts)
      setattr(opts, "ddp.rank", node_rank)
      main(opts, **kwargs)
  
file_path: ml-cvnets/main_loss_landscape.py
imports: from typing import List, Optional
  import torch
  from cvnets import get_model
  from data import create_test_loader
  from engine import Trainer
  from loss_fn import build_loss_fn
  from options.opts import get_loss_landscape_args
  from utils import logger, resources
  from utils.common_utils import create_directories, device_setup
  from utils.ddp_utils import distributed_init, is_master
label_desc: Use image node_id 2 for calling a global function with example usage: distributed_worker(i, main, opts, kwargs) without return types