class_docstr: WideDeep is a context-based recommendation model.
  It jointly trains wide linear models and deep neural networks to combine the benefits
  of memorization and generalization for recommender systems. The wide component is a generalized linear model
  of the form :math:`y = w^Tx + b`. The deep component is a feed-forward neural network. The wide component
  and deep component are combined using a weighted sum of their output log odds as the prediction,
  which is then fed to one common logistic loss function for joint training.
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code: def predict(self, interaction):
      return self.sigmoid(self.forward(interaction))
  
file_path: RecBole/recbole/model/context_aware_recommender/widedeep.py
imports: import torch.nn
  from torch.nn.init import xavier_normal_, constant_
  from recbole.model.abstract_recommender import ContextRecommender
  from recbole.model.layers import MLPLayers
label_desc: Use image node_id 5 for calling the WideDeep obj's underlying member method code with example usage: obj.predict(interaction) and returns: self