class_docstr: SHAN exploit the Hierarchical Attention Network to get the long-short term preference
  first get the long term purpose and then fuse the long-term with recent items to get long-short term purpose
class_docstr_tok: ["SHAN","exploit","the","Hierarchical","Attention","Network","to","get","the","long-short","term","preference","first","get","the","long","term","purpose","and","then","fuse","the","long-term","with","recent","items","to","get","long-short","term","purpose"]
code: def predict(self, interaction):
      inverse_item_seq = interaction[self.INVERSE_ITEM_SEQ]
      test_item = interaction[self.ITEM_ID]
      user = interaction[self.USER_ID]
      seq_output = self.forward(inverse_item_seq, user)
      test_item_emb = 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/shan.py
imports: import numpy
  import torch
  import torch.nn
  from torch.nn.init import normal_, uniform_
  from recbole.model.abstract_recommender import SequentialRecommender
  from recbole.model.loss import BPRLoss
label_desc: Use image node_id 6 for calling the SHAN obj's underlying member method code with example usage: obj.predict(interaction) and returns: scores