code: def _compute_loss(self, mat, pos_mask, neg_mask):
      remaining_pos_margin = self.distance.margin(mat, self.pos_margin)
      remaining_neg_margin = self.distance.margin(self.neg_margin, mat)
  
      pos_loss = lmu.logsumexp(
          remaining_pos_margin, keep_mask=pos_mask.bool(), add_one=False
      )
      neg_loss = lmu.logsumexp(
          remaining_neg_margin, keep_mask=neg_mask.bool(), add_one=False
      )
      return {
          "loss": {
              "losses": torch.relu(pos_loss + neg_loss),
              "indices": c_f.torch_arange_from_size(mat),
              "reduction_type": "element",
          }
      }
  
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 GeneralizedLiftedStructureLoss obj's underlying member method code with example usage: obj._compute_loss(mat, pos_mask, neg_mask) and returns: dict