code: def concat_all_gather(tensor):
      """
      Performs all_gather operation on the provided tensors.
      *** Warning ***: torch.distributed.all_gather has no gradient.
      """
      # if use distributed training
      if not is_dist_avail_and_initialized():
          return tensor
  
      tensors_gather = [
          torch.ones_like(tensor)
          for _ in range(torch.distributed.get_world_size())
      ]
      torch.distributed.all_gather(
          tensors_gather, tensor, async_op=False
      )
  
      output = torch.cat(tensors_gather, dim=0)
      return output
  
imports: import logging
  import os
  import numpy
  import torch
  import torch.nn
  from lavis.common.dist_utils import download_cached_file, is_dist_avail_and_initialized
  from lavis.common.utils import get_abs_path, is_url
  from omegaconf import OmegaConf
label_desc: Use image node_id 2 for calling a global function with example usage: concat_all_gather(tensor) and returns: output, tensor