code: def create_efficient_batch(
      all_outputs, all_ref_outputs, i, batch_size
  ):
      curr_source = all_outputs
      other_source = (
          all_outputs if all_ref_outputs is None else all_ref_outputs
      )
      s = i * batch_size
      e = (i + 1) * batch_size
      curr = curr_source[s:e]
      others = torch.cat(
          [other_source[:s], other_source[e:]], dim=0
      ).detach()
      if all_ref_outputs is None:
          others = torch.cat([curr, others], dim=0)
      else:
          others = torch.cat([other_source[s:e], others], dim=0)
      return curr, others
  
imports: import logging
  import os
  import unittest
  import torch
  import torch.distributed
  import torch.multiprocessing
  import torch.optim
  from torch.nn.parallel import DistributedDataParallel
  from pytorch_metric_learning.losses import ContrastiveLoss, CrossBatchMemory
  from pytorch_metric_learning.miners import PairMarginMiner
  from pytorch_metric_learning.utils import distributed
  from ..None import TEST_DEVICE, TEST_DTYPES
label_desc: Use image node_id 5 for calling a global function with example usage: create_efficient_batch(all_outputs, all_ref_outputs, i, batch_size) and returns: curr,  others