code: def training(local_rank, config, **kwargs):
      import time
  
      time.sleep(idist.get_rank() * 0.1)
  
      print(
          idist.get_rank(),
          ": run with config:",
          config,
          "- kwargs:",
          kwargs,
          f"- backend={idist.backend()}",
      )
  
      t = torch.tensor([idist.get_rank()], device=idist.device())
      t = idist.all_reduce(t)
      t = t.item()
      ws = idist.get_world_size()
      assert t == ws * (ws - 1) / 2, f"{t} vs {ws}"
      assert local_rank == idist.get_local_rank()
  
      # Test init method:
      if idist.model_name() == "native-dist":
          from ignite.distributed.utils import _model
  
          true_init_method = config.get("true_init_method", None)
          assert true_init_method is not None, true_init_method
          assert _model._init_method == true_init_method
  
label_desc: Use image node_id 1 for calling a global function with example usage: training(local_rank, config) without return types