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 full_sort_predict(self, interaction):
      inverse_item_seq = interaction[self.ITEM_SEQ]
      user = interaction[self.USER_ID]
      seq_output = self.forward(inverse_item_seq, user)
      test_items_emb = self.item_embedding.weight
      scores = torch.matmul(seq_output, test_items_emb.transpose(0, 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 7 for calling the SHAN obj's underlying member method code with example usage: obj.full_sort_predict(interaction) and returns: scores