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 final L2 normalization loss of model parameters.
  Including weight matrices of mlp layers, linear layer and convolutional layers.
  
  Returns:
      loss(torch.FloatTensor): The L2 Loss tensor. shape of [1,]
docstr_tok: ["Calculate","the","final","L2","normalization","loss","of","model","parameters",".","Including","weight","matrices","of","mlp","layers",",","linear","layer","and","convolutional","layers",".","Returns",":","loss","(","torch.FloatTensor",")",":","The","L2","Loss","tensor",".","shape","of","[","1",",","]"]
code: def calculate_reg_loss(self):
      """Calculate the final L2 normalization loss of model parameters.
      Including weight matrices of mlp layers, linear layer and convolutional layers.
  
      Returns:
          loss(torch.FloatTensor): The L2 Loss tensor. shape of [1,]
      """
      l2_reg = 0
      l2_reg = l2_reg + self.reg_loss(
          self.mlp_layers.named_parameters()
      )
      l2_reg = l2_reg + self.reg_loss(
          self.first_order_linear.named_parameters()
      )
      for conv1d in self.conv1d_list:
          l2_reg += self.reg_loss(conv1d.named_parameters())
      return l2_reg
  
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 4 for calling the xDeepFM obj's underlying member method code with example usage: obj.calculate_reg_loss() and returns: l2_reg