code: def compute_loss(
      self, embeddings, labels, indices_tuple, ref_emb, ref_labels
  ):
      c_f.labels_or_indices_tuple_required(labels, indices_tuple)
      indices_tuple = lmu.convert_to_pairs(
          indices_tuple, labels, ref_labels
      )
      if all(len(x) <= 1 for x in indices_tuple):
          return self.zero_losses()
      mat = self.distance(embeddings, ref_emb)
      return self.loss_method(mat, 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 2 for calling the GenericPairLoss obj's underlying member method code with example usage: obj.compute_loss(embeddings, labels, indices_tuple, ref_emb, ref_labels) and returns: self, self