class_docstr: models a user’s click to an item in two terms: the personal preference of the user for the item,
  and the relationships between this item and other items clicked by the user
class_docstr_tok: ["models","a","user","\u2019","s","click","to","an","item","in","two","terms",":","the","personal","preference","of","the","user","for","the","item",",","and","the","relationships","between","this","item","and","other","items","clicked","by","the","user"]
code: def predict(self, interaction):
      item_seq = interaction[self.ITEM_SEQ]
      test_item = interaction[self.ITEM_ID]
      user = interaction[self.USER_ID]
      seq_output = self.forward(item_seq, user)
      test_item_emb = self.relu(self.item_embedding(test_item))
      scores = torch.mul(seq_output, test_item_emb).sum(dim=1)
      return scores
  
file_path: RecBole/recbole/model/sequential_recommender/npe.py
imports: import torch
  import torch.nn
  from torch.nn.init import xavier_normal_
  from recbole.model.abstract_recommender import SequentialRecommender
  from recbole.model.loss import BPRLoss
label_desc: Use image node_id 5 for calling the NPE obj's underlying member method code with example usage: obj.predict(interaction) and returns: scores