class_docstr: FISM is an item-based model for generating top-N recommendations that learns the
  item-item similarity matrix as the product of two low dimensional latent factor matrices.
  These matrices are learned using a structural equation modeling approach, where in the
  value being estimated is not used for its own estimation.
class_docstr_tok: ["FISM","is","an","item-based","model","for","generating","top-N","recommendations","that","learns","the","item-item","similarity","matrix","as","the","product","of","two","low","dimensional","latent","factor","matrices",".","These","matrices","are","learned","using","a","structural","equation","modeling","approach",",","where","in","the","value","being","estimated","is","not","used","for","its","own","estimation","."]
docstr: calculate the reg loss for embedding layers
  
  Returns:
      torch.Tensor: reg loss
docstr_tok: ["calculate","the","reg","loss","for","embedding","layers","Returns",":","torch.Tensor",":","reg","loss"]
code: def reg_loss(self):
      """calculate the reg loss for embedding layers
  
      Returns:
          torch.Tensor: reg loss
  
      """
      reg_1, reg_2 = self.reg_weights
      loss_1 = reg_1 * self.item_src_embedding.weight.norm(2)
      loss_2 = reg_2 * self.item_dst_embedding.weight.norm(2)
  
      return loss_1 + loss_2
  
file_path: RecBole/recbole/model/general_recommender/fism.py
imports: import torch
  import torch.nn
  from torch.nn.init import normal_
  from recbole.model.abstract_recommender import GeneralRecommender
  from recbole.utils import InputType
label_desc: Use image node_id 3 for calling the FISM obj's underlying member method code with example usage: obj.reg_loss() and returns: unknown