code: def forward(self, emb, labels, ref_emb=None, ref_labels=None):
      world_size = torch.distributed.get_world_size()
      if world_size <= 1:
          return self.miner(emb, labels, ref_emb, ref_labels)
  
      (
          all_emb,
          all_labels,
          all_ref_emb,
          all_ref_labels,
          labels,
      ) = gather_emb_and_ref(emb, labels, ref_emb, ref_labels)
      if self.efficient:
          all_labels = select_ref_or_regular(all_labels, all_ref_labels)
          all_emb = select_ref_or_regular(all_emb, all_ref_emb)
          return get_indices_tuple(
              labels, all_labels, emb, all_emb, self.miner
          )
      else:
          return self.miner(
              all_emb, all_labels, all_ref_emb, all_ref_labels
          )
  
imports: import torch
  from ..losses import BaseMetricLossFunction, CrossBatchMemory
  from ..miners import BaseMiner
  from ..utils import common_functions
  from ..utils import loss_and_miner_utils
label_desc: Use image node_id 2 for calling the DistributedMinerWrapper obj's underlying member method code with example usage: obj.forward(emb, labels, ref_emb, ref_labels) and returns: self, get_indices_tuple, self