code: def _compute_loss(self, pos_pairs, neg_pairs, indices_tuple):
      a1, p, a2, _ = indices_tuple
  
      if len(a1) > 0 and len(a2) > 0:
          pos_pairs = pos_pairs.unsqueeze(1)
          dtype = pos_pairs.dtype
          n_per_p = c_f.to_dtype(
              (a2.unsqueeze(0) == a1.unsqueeze(1))
              | (a2.unsqueeze(0) == p.unsqueeze(1)),
              dtype=dtype,
          )
          neg_pairs = neg_pairs * n_per_p
          keep_mask = ~(n_per_p == 0)
  
          remaining_pos_margin = self.distance.margin(
              pos_pairs, self.pos_margin
          )
          remaining_neg_margin = self.distance.margin(
              self.neg_margin, neg_pairs
          )
  
          neg_pairs_loss = lmu.logsumexp(
              remaining_neg_margin,
              keep_mask=keep_mask,
              add_one=False,
              dim=1,
          )
          loss_per_pos_pair = neg_pairs_loss + remaining_pos_margin
          loss_per_pos_pair = torch.relu(loss_per_pos_pair) ** 2
          loss_per_pos_pair /= 2  # divide by 2 since each positive pair will be counted twice
          return {
              "loss": {
                  "losses": loss_per_pos_pair,
                  "indices": (a1, p),
                  "reduction_type": "pos_pair",
              }
          }
      return self.zero_losses()
  
file_path: pytorch-metric-learning/src/pytorch_metric_learning/losses/lifted_structure_loss.py
imports: import torch
  from ..utils import common_functions
  from ..utils import loss_and_miner_utils
  from .generic_pair_loss import GenericPairLoss
label_desc: Use image node_id 2 for calling the LiftedStructureLoss obj's underlying member method code with example usage: obj._compute_loss(pos_pairs, neg_pairs, indices_tuple) and returns: self, dict