code: def concat_all_gather(tensor):
      """
      Performs all_gather operation on the provided tensors.
      *** Warning ***: torch.distributed.all_gather has no gradient.
      """
      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 collections
  import collections.abc
  import logging
  import os
  import random
  import sys
  import tempfile
  import time
  from functools import partial, wraps
  from typing import Tuple
  import numpy
  import pkg_resources
  import torch
  import torch.multiprocessing
  from iopath.common.file_io import g_pathmgr
  from scipy.sparse import csr_matrix
  from vissl.utils.extract_features_utils import ExtractedFeaturesLoader
label_desc: Use image node_id 15 for calling a global function with example usage: concat_all_gather(tensor) and returns: output