code: def test_distributed_tuple_loss_and_miner(self):
      for xbm in [False, True]:
          for use_ref in [False, True]:
              for pass_labels_to_loss_fn in [False, True]:
                  if (
                      xbm
                      and use_ref
                      or xbm
                      and not pass_labels_to_loss_fn
                  ):
                      continue
                  self.loss_and_miner_tester(
                      ContrastiveLoss,
                      PairMarginMiner,
                      False,
                      xbm,
                      use_ref,
                      miner_kwargs={
                          "pos_margin": 0.5,
                          "neg_margin": 0.5,
                      },
                      pass_labels_to_loss_fn=pass_labels_to_loss_fn,
                  )
  
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 3 for calling the TestDistributedLossWrapper obj's underlying member method code with example usage: obj.test_distributed_tuple_loss_and_miner() without return types