code: def _compute_loss(self, pos_pair_dist, neg_pair_dist, indices_tuple):
      pos_loss, neg_loss = 0, 0
      if len(pos_pair_dist) > 0:
          pos_loss = self.get_per_pair_loss(pos_pair_dist, "pos")
      if len(neg_pair_dist) > 0:
          neg_loss = self.get_per_pair_loss(neg_pair_dist, "neg")
      pos_pairs = lmu.pos_pairs_from_tuple(indices_tuple)
      neg_pairs = lmu.neg_pairs_from_tuple(indices_tuple)
      return {
          "pos_loss": {
              "losses": pos_loss,
              "indices": pos_pairs,
              "reduction_type": "pos_pair",
          },
          "neg_loss": {
              "losses": neg_loss,
              "indices": neg_pairs,
              "reduction_type": "neg_pair",
          },
      }
  
file_path: pytorch-metric-learning/src/pytorch_metric_learning/losses/contrastive_loss.py
imports: import torch
  from ..reducers import AvgNonZeroReducer
  from ..utils import loss_and_miner_utils
  from .generic_pair_loss import GenericPairLoss
label_desc: Use image node_id 2 for calling the ContrastiveLoss obj's underlying member method code with example usage: obj._compute_loss(pos_pair_dist, neg_pair_dist, indices_tuple) and returns: dict