code: def synchronize_between_processes(self):
      if dist.is_initialized():
          # Bypass NCCL (which forces CUDA-only sync)
          if dist.get_backend() == "nccl":
              group = dist.new_group(backend="gloo")
          else:
              group = dist.group.WORLD
  
          my_rank = dist.get_rank()
          output = [None for _ in range(dist.get_world_size())]
          dist.gather_object(
              self.predictions,
              output if my_rank == 0 else None,
              dst=0,
              group=group,
          )
  
          return (
              list(itertools.chain.from_iterable(output)),
              my_rank == 0,
          )
      else:
          return self.predictions, True
  
imports: import copy
  import itertools
  import logging
  from typing import List
  import numpy
  import pycocotools.mask
  import torch
  import torch.distributed
  from lvis import LVISEval, LVISResults, LVIS
label_desc: Use image node_id 3 for calling the LvisEvaluator obj's underlying member method code with example usage: obj.synchronize_between_processes() and returns: list,  unknown, self,  True