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_triplets(
          indices_tuple, labels, ref_labels, self.triplets_per_anchor
      )
      anchor_idx, positive_idx, negative_idx = indices_tuple
      if len(anchor_idx) == 0:
          return self.zero_losses()
  
      beta = (
          self.beta
          if len(self.beta) == 1
          else self.beta[labels[anchor_idx]]
      )
      beta = c_f.to_device(
          beta, device=embeddings.device, dtype=embeddings.dtype
      )
  
      mat = self.distance(embeddings, ref_emb)
  
      d_ap = mat[anchor_idx, positive_idx]
      d_an = mat[anchor_idx, negative_idx]
  
      pos_loss = torch.nn.functional.relu(
          self.distance.margin(d_ap, beta) + self.margin
      )
      neg_loss = torch.nn.functional.relu(
          self.distance.margin(beta, d_an) + self.margin
      )
  
      num_pos_pairs = torch.sum(pos_loss > 0.0)
      num_neg_pairs = torch.sum(neg_loss > 0.0)
  
      divisor = num_pos_pairs + num_neg_pairs
  
      margin_loss = pos_loss + neg_loss
  
      loss_dict = {
          "margin_loss": {
              "losses": margin_loss,
              "indices": indices_tuple,
              "reduction_type": "triplet",
              "divisor": divisor,
          },
          "beta_reg_loss": self.compute_reg_loss(
              beta, anchor_idx, divisor
          ),
      }
  
      return loss_dict
  
file_path: pytorch-metric-learning/src/pytorch_metric_learning/losses/margin_loss.py
imports: import torch
  from ..reducers import DivisorReducer
  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 MarginLoss obj's underlying member method code with example usage: obj.compute_loss(embeddings, labels, indices_tuple, ref_emb, ref_labels) and returns: loss_dict, self