code: def calculate_reg_loss(self):
      C_mean = torch.mean(self.C.weight, dim=1, keepdim=True)
      C_reg = self.C.weight - C_mean
      C_reg = C_reg.matmul(C_reg.T) / self.D
      return (
          torch.norm(C_reg) ** 2 - torch.norm(torch.diag(C_reg)) ** 2
      ) / 2
  
file_path: RecBole/recbole/model/sequential_recommender/sine.py
imports: import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn.init import xavier_normal_
  from recbole.model.abstract_recommender import SequentialRecommender
  from recbole.model.loss import BPRLoss
  from recbole.utils import InputType
label_desc: Use image node_id 5 for calling the SINE obj's underlying member method code with example usage: obj.calculate_reg_loss() and returns: unknown