code: def test_distributed_tuple_loss(self):
      for xbm in [False, True]:
          for use_ref in [False, True]:
              for use_xbm_enqueue_mask in [False, True]:
                  if xbm and use_ref:
                      continue
                  self.loss_and_miner_tester(
                      ContrastiveLoss,
                      None,
                      False,
                      xbm,
                      use_ref,
                      use_xbm_enqueue_mask=use_xbm_enqueue_mask,
                  )
  
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 2 for calling the TestDistributedLossWrapper obj's underlying member method code with example usage: obj.test_distributed_tuple_loss() without return types