code: def pair_based_loss(self, mat, indices_tuple):
      a1, p, a2, n = indices_tuple
      pos_pair, neg_pair = [], []
      if len(a1) > 0:
          pos_pair = mat[a1, p]
      if len(a2) > 0:
          neg_pair = mat[a2, n]
      return self._compute_loss(pos_pair, neg_pair, indices_tuple)
  
file_path: pytorch-metric-learning/src/pytorch_metric_learning/losses/generic_pair_loss.py
imports: import torch
  from ..utils import common_functions
  from ..utils import loss_and_miner_utils
  from .base_metric_loss_function import BaseMetricLossFunction
label_desc: Use image node_id 5 for calling the GenericPairLoss obj's underlying member method code with example usage: obj.pair_based_loss(mat, indices_tuple) and returns: self