code: def mat_based_loss(self, mat, indices_tuple):
      a1, p, a2, n = indices_tuple
      pos_mask, neg_mask = torch.zeros_like(mat), torch.zeros_like(mat)
      pos_mask[a1, p] = 1
      neg_mask[a2, n] = 1
      return self._compute_loss(mat, pos_mask, neg_mask)
  
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 4 for calling the GenericPairLoss obj's underlying member method code with example usage: obj.mat_based_loss(mat, indices_tuple) and returns: self