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.
      Args:
          data (Any): data from the local worker to be gathered on other workers
          group (optional): group of the collective
      """
      SIZE_STORAGE_BYTES = 4  # int32 to encode the payload size
  
      enc = pickle.dumps(data)
      enc_size = len(enc)
  
      if enc_size + SIZE_STORAGE_BYTES > max_size:
          raise ValueError(
              "encoded data exceeds max_size, this can be fixed by increasing buffer size: {}".format(
                  enc_size
              )
          )
  
      rank = dist.get_rank()
      world_size = dist.get_world_size()
      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
  
      assert (
          enc_size < 256**SIZE_STORAGE_BYTES
      ), "Encoded object size should be less than {} bytes".format(
          256**SIZE_STORAGE_BYTES
      )
  
      size_bytes = enc_size.to_bytes(
          SIZE_STORAGE_BYTES, byteorder="big"
      )
  
      cpu_buffer[0:SIZE_STORAGE_BYTES] = torch.ByteTensor(
          list(size_bytes)
      )
      cpu_buffer[
          SIZE_STORAGE_BYTES : enc_size + SIZE_STORAGE_BYTES
      ] = torch.ByteTensor(list(enc))
  
      start = rank * max_size
      size = enc_size + SIZE_STORAGE_BYTES
      buffer[start : start + size].copy_(cpu_buffer[:size])
  
      all_reduce(buffer, group=group)
  
      try:
          result = []
          for i in range(world_size):
              out_buffer = buffer[i * max_size : (i + 1) * max_size]
              size = int.from_bytes(
                  out_buffer[0:SIZE_STORAGE_BYTES], byteorder="big"
              )
              if size > 0:
                  result.append(
                      pickle.loads(
                          bytes(
                              out_buffer[
                                  SIZE_STORAGE_BYTES : size
                                  + SIZE_STORAGE_BYTES
                              ].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."
          )
  
imports: import hashlib
  import json
  import logging
  import random
  import os
  import signal
  import numpy
  import torch
  import torch.distributed
  from requests.exceptions import ConnectionError
  from torch import multiprocessing
  import mlflow
  from copy import deepcopy
  import pandas
  from tqdm import tqdm
  import time
  import pickle
  from farm.visual.ascii.images import WELCOME_BARN, WORKER_M, WORKER_F, WORKER_X
label_desc: Use image node_id 17 for calling a global function with example usage: all_gather_list(data, group, max_size) and returns: result