code: def compute_reg_loss(self, beta, anchor_idx, divisor):
      if self.learn_beta:
          loss = beta * self.nu
          if len(self.beta) == 1:
              return {
                  "losses": loss,
                  "indices": None,
                  "reduction_type": "already_reduced",
              }
          else:
              return {
                  "losses": loss,
                  "indices": anchor_idx,
                  "reduction_type": "element",
                  "divisor": divisor,
              }
      return self.zero_loss()
  
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 3 for calling the MarginLoss obj's underlying member method code with example usage: obj.compute_reg_loss(beta, anchor_idx, divisor) and returns: self, dict, dict