code: def all_gather_list(data, group=None, max_size=16384):
      """Gathers arbitrary data from all nodes into a list.
  
      Similar to :func:`~torch.distributed.all_gather` but for arbitrary Python
      data. Note that *data* must be picklable and any CUDA tensors will be moved
      to CPU and returned on CPU as well.
  
      Args:
          data (Any): data from the local worker to be gathered on other workers
          group: group of the collective
          max_size (int, optional): maximum size of the data to be gathered
              across workers
      """
      from fairseq import utils
  
      if group is None:
          group = get_global_group()
      rank = get_rank(group=group)
      world_size = get_world_size(group=group)
  
      buffer_size = max_size * world_size
      if (
          not hasattr(all_gather_list, "_buffer")
          or all_gather_list._buffer.numel() < buffer_size
      ):
          all_gather_list._buffer = torch.cuda.ByteTensor(buffer_size)
          all_gather_list._cpu_buffer = torch.ByteTensor(
              max_size
          ).pin_memory()
      buffer = all_gather_list._buffer
      buffer.zero_()
      cpu_buffer = all_gather_list._cpu_buffer
  
      data = utils.move_to_cpu(data)
      enc = pickle.dumps(data)
      enc_size = len(enc)
      header_size = 4  # size of header that contains the length of the encoded data
      size = header_size + enc_size
      if size > max_size:
          raise ValueError(
              "encoded data size ({}) exceeds max_size ({})".format(
                  size, max_size
              )
          )
  
      header = struct.pack(">I", enc_size)
      cpu_buffer[:size] = torch.ByteTensor(list(header + enc))
      start = rank * max_size
      buffer[start : start + size].copy_(cpu_buffer[:size])
  
      all_reduce(buffer, group=group)
  
      buffer = buffer.cpu()
      try:
          result = []
          for i in range(world_size):
              out_buffer = buffer[i * max_size : (i + 1) * max_size]
              (enc_size,) = struct.unpack(
                  ">I", bytes(out_buffer[:header_size].tolist())
              )
              if enc_size > 0:
                  result.append(
                      pickle.loads(
                          bytes(
                              out_buffer[
                                  header_size : header_size + enc_size
                              ].tolist()
                          )
                      )
                  )
          return result
      except pickle.UnpicklingError:
          raise Exception(
              "Unable to unpickle data from other workers. all_gather_list requires all "
              "workers to enter the function together, so this error usually indicates "
              "that the workers have fallen out of sync somehow. Workers can fall out of "
              "sync if one of them runs out of memory, or if there are other conditions "
              "in your training script that can cause one worker to finish an epoch "
              "while other workers are still iterating over their portions of the data. "
              "Try rerunning with --ddp-backend=legacy_ddp and see if that helps."
          )
  
imports: import io
  import logging
  import os
  import pickle
  import random
  import socket
  import struct
  import subprocess
  import warnings
  from argparse import Namespace
  from collections import OrderedDict
  from dataclasses import dataclass
  from typing import Any, Dict, List, Mapping, Optional
  import torch
  import torch.distributed
  from fairseq.dataclass.configs import DistributedTrainingConfig, FairseqConfig
  from omegaconf import open_dict
label_desc: Use image node_id 30 for calling a global function with example usage: all_gather_list(data, group, max_size) and returns: result