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 forward(self, seq_item, user):
      seq_item_embedding = self.item_embedding(seq_item)
      user_embedding = self.user_embedding(user)
  
      # get the mask
      mask = seq_item.data.eq(0)
      long_term_attention_based_pooling_layer = (
          self.long_term_attention_based_pooling_layer(
              seq_item_embedding, user_embedding, mask
          )
      )
      # batch_size * 1 * embedding_size
  
      short_item_embedding = seq_item_embedding[
          :, -self.short_item_length :, :
      ]
      mask_long_short = mask[:, -self.short_item_length :]
      batch_size = mask_long_short.size(0)
      x = torch.zeros(size=(batch_size, 1)).eq(1).to(self.device)
      mask_long_short = torch.cat([x, mask_long_short], dim=1)
      # batch_size * short_item_length * embedding_size
      long_short_item_embedding = torch.cat(
          [
              long_term_attention_based_pooling_layer,
              short_item_embedding,
          ],
          dim=1,
      )
      # batch_size * 1_plus_short_item_length * embedding_size
  
      long_short_item_embedding = (
          self.long_and_short_term_attention_based_pooling_layer(
              long_short_item_embedding, user_embedding, mask_long_short
          )
      )
      # batch_size * embedding_size
  
      return long_short_item_embedding
  
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 4 for calling the SHAN obj's underlying member method code with example usage: obj.forward(seq_item, user) and returns: long_short_item_embedding