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.
class_docstr_tok: ["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","."]
code: def predict(self, interaction):
      return self.sigmoid(self.forward(interaction))
  
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 8 for calling the xDeepFM obj's underlying member method code with example usage: obj.predict(interaction) and returns: self