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_angles = torch.acos(pos_pairs)
          self.set_stats(pos_angles, neg_pairs)
          pos_pairs = torch.cos(pos_angles - self.margin)
          pos_pairs = pos_pairs.unsqueeze(1)
          neg_pairs = neg_pairs.repeat(pos_pairs.size(0), 1)
          inside_exp = self.scale * (neg_pairs - pos_pairs)
          keep_mask = a2.unsqueeze(0) == a1.unsqueeze(1)
          loss = lmu.logsumexp(
              inside_exp, keep_mask=keep_mask, add_one=True, dim=1
          )
          return {
              "loss": {
                  "losses": loss,
                  "indices": (a1, p),
                  "reduction_type": "pos_pair",
              }
          }
      return self.zero_losses()
  
file_path: pytorch-metric-learning/src/pytorch_metric_learning/losses/tuplet_margin_loss.py
imports: import numpy
  import torch
  from ..distances import CosineSimilarity
  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 TupletMarginLoss obj's underlying member method code with example usage: obj._compute_loss(pos_pairs, neg_pairs, indices_tuple) and returns: self, dict