class_docstr: PNN calculate inner and outer product of feature embedding.
  You can choose the product option with the parameter of use_inner and use_outer
class_docstr_tok: ["PNN","calculate","inner","and","outer","product","of","feature","embedding",".","You","can","choose","the","product","option","with","the","parameter","of","use_inner","and","use_outer"]
code: def predict(self, interaction):
      return self.sigmoid(self.forward(interaction))
  
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
label_desc: Use image node_id 6 for calling the PNN obj's underlying member method code with example usage: obj.predict(interaction) and returns: self