code: def all_gather_list(data, max_size=4096):
      """Gathers arbitrary data from all nodes into a list."""
      world_size = torch.distributed.get_world_size()
      if (
          not hasattr(all_gather_list, "_in_buffer")
          or max_size != all_gather_list._in_buffer.size()
      ):
          all_gather_list._in_buffer = torch.cuda.ByteTensor(max_size)
          all_gather_list._out_buffers = [
              torch.cuda.ByteTensor(max_size) for i in range(world_size)
          ]
      in_buffer = all_gather_list._in_buffer
      out_buffers = all_gather_list._out_buffers
  
      enc = pickle.dumps(data)
      enc_size = len(enc)
      if enc_size + 2 > max_size:
          raise ValueError(
              "encoded data exceeds max_size: {}".format(enc_size + 2)
          )
      assert max_size < 255 * 256
      in_buffer[0] = (
          enc_size // 255
      )  # this encoding works for max_size < 65k
      in_buffer[1] = enc_size % 255
      in_buffer[2 : enc_size + 2] = torch.ByteTensor(list(enc))
  
      torch.distributed.all_gather(out_buffers, in_buffer.cuda())
  
      results = []
      for i in range(world_size):
          out_buffer = out_buffers[i]
          size = (255 * out_buffer[0].item()) + out_buffer[1].item()
  
          bytes_list = bytes(out_buffer[2 : size + 2].tolist())
          result = pickle.loads(bytes_list)
          results.append(result)
      return results
  
imports: import os
  import signal
  import math
  import pickle
  import torch.distributed
  from datetime import timedelta
  from onmt.translate.translator import build_translator
  from onmt.transforms import get_transforms_cls
  from onmt.constants import CorpusTask
  from onmt.utils.logging import init_logger, logger
  from onmt.inputters.dynamic_iterator import build_dynamic_dataset_iter
label_desc: Use image node_id 4 for calling a global function with example usage: all_gather_list(data, max_size) and returns: results