code: def run_distributed_test(
      device_ids: List[int] = None,
      func: Callable = None,
      *args,
      **kwargs,
  ):
      """
      This runs the `func` in a simulated distributed environment.
  
      # Parameters
  
      device_ids: `List[int]`
          List of devices. There need to be at least 2 devices. Default is [-1, -1].
  
      func: `Callable`
          `func` needs to be global for spawning the processes, so that it can be pickled.
  
      start_method: `Optional[str]`, optional (default = `None`)
          The start method to use for starting the workers. Defaults to "spawn" for GPU
          processes and fork otherwise.
      """
      device_ids = device_ids or [-1, -1]
      check_for_gpu(device_ids)
      # "fork" start method is the default and should be preferred, except when we're
      # running the tests on GPU, in which case we need to use "spawn".
      if "start_method" in kwargs:
          start_method = kwargs.pop("start_method")
      else:
          start_method = (
              "spawn" if any(x >= 0 for x in device_ids) else "fork"
          )
      nprocs = world_size = len(device_ids)
      mp.start_processes(
          init_process,
          args=(world_size, device_ids, func, args, kwargs),
          nprocs=nprocs,
          start_method=start_method,
      )
  
imports: import datetime
  from typing import List, Dict, Any, Tuple, Callable
  import torch
  import torch.distributed
  import torch.multiprocessing
  from allennlp.common.checks import check_for_gpu
label_desc: Use image node_id 2 for calling a global function with example usage: run_distributed_test(device_ids, func) without return types