class_docstr: xDeepFM combines a CIN (Compressed Interaction Network) with a classical DNN.
  The model is able to learn certain bounded-degree feature interactions explicitly;
  Besides, it can also learn arbitrary low- and high-order feature interactions implicitly.
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docstr: Calculate the L2 normalization loss of parameters in a certain layer.
  
  Returns:
      loss(torch.FloatTensor): The L2 Loss tensor. shape of [1,]
docstr_tok: ["Calculate","the","L2","normalization","loss","of","parameters","in","a","certain","layer",".","Returns",":","loss","(","torch.FloatTensor",")",":","The","L2","Loss","tensor",".","shape","of","[","1",",","]"]
code: def reg_loss(self, parameters):
      """Calculate the L2 normalization loss of parameters in a certain layer.
  
      Returns:
          loss(torch.FloatTensor): The L2 Loss tensor. shape of [1,]
      """
      reg_loss = 0
      for name, parm in parameters:
          if name.endswith("weight"):
              reg_loss = reg_loss + parm.norm(2)
      return reg_loss
  
file_path: RecBole/recbole/model/context_aware_recommender/xdeepfm.py
imports: import torch
  import torch.nn
  from torch.nn.init import xavier_normal_, constant_
  from recbole.model.abstract_recommender import ContextRecommender
  from recbole.model.layers import MLPLayers, activation_layer
label_desc: Use image node_id 3 for calling the xDeepFM obj's underlying member method code with example usage: obj.reg_loss(parameters) and returns: reg_loss