class_docstr: Deep & Cross Network replaces the wide part in Wide&Deep with cross network,
  automatically construct limited high-degree cross features, and learns the corresponding weights.
class_docstr_tok: ["Deep","&","Cross","Network","replaces","the","wide","part","in","Wide","&","Deep","with","cross","network",",","automatically","construct","limited","high-degree","cross","features",",","and","learns","the","corresponding","weights","."]
code: def predict(self, interaction):
      return self.sigmoid(self.forward(interaction))
  
file_path: RecBole/recbole/model/context_aware_recommender/dcn.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
  from recbole.model.loss import RegLoss
label_desc: Use image node_id 6 for calling the DCN obj's underlying member method code with example usage: obj.predict(interaction) and returns: self