class_docstr: DCNV2 improves the cross network by extending the original weight vector to a matrix,
  significantly improves the expressiveness of DCN. It also introduces the MoE and
  low rank techniques to reduce time cost.
class_docstr_tok: ["DCNV2","improves","the","cross","network","by","extending","the","original","weight","vector","to","a","matrix",",","significantly","improves","the","expressiveness","of","DCN",".","It","also","introduces","the","MoE","and","low","rank","techniques","to","reduce","time","cost","."]
code: def calculate_loss(self, interaction):
      label = interaction[self.LABEL]
      output = self.forward(interaction)
      if self.mixed:
          reg_loss = (
              self.reg_loss(self.cross_layer_c)
              + self.reg_loss(self.cross_layer_v)
              + self.reg_loss(self.cross_layer_u)
          )
      else:
          reg_loss = self.reg_loss(self.cross_layer_w)
      l2_loss = self.reg_weight * reg_loss
      return self.loss(output, label) + l2_loss
  
file_path: RecBole/recbole/model/context_aware_recommender/dcnv2.py
imports: import torch
  import torch.nn
  from recbole.model.abstract_recommender import ContextRecommender
  from recbole.model.init import xavier_normal_initialization
  from recbole.model.layers import MLPLayers
  from recbole.model.loss import RegLoss
label_desc: Use image node_id 5 for calling the DCNV2 obj's underlying member method code with example usage: obj.calculate_loss(interaction) and returns: unknown