class_docstr: STAMP is capable of capturing users’ general interests from the long-term memory of a session context,
  whilst taking into account users’ current interests from the short-term memory of the last-clicks.
  
  
  Note:
  
      According to the test results, we made a little modification to the score function mentioned in the paper,
      and did not use the final sigmoid activation function.
class_docstr_tok: ["STAMP","is","capable","of","capturing","users","\u2019","general","interests","from","the","long-term","memory","of","a","session","context",",","whilst","taking","into","account","users","\u2019","current","interests","from","the","short-term","memory","of","the","last-clicks",".","Note",":","According","to","the","test","results",",","we","made","a","little","modification","to","the","score","function","mentioned","in","the","paper",",","and","did","not","use","the","final","sigmoid","activation","function","."]
code: def calculate_loss(self, interaction):
      item_seq = interaction[self.ITEM_SEQ]
      item_seq_len = interaction[self.ITEM_SEQ_LEN]
      seq_output = self.forward(item_seq, item_seq_len)
      pos_items = interaction[self.POS_ITEM_ID]
      if self.loss_type == "BPR":
          neg_items = interaction[self.NEG_ITEM_ID]
          pos_items_emb = self.item_embedding(pos_items)
          neg_items_emb = self.item_embedding(neg_items)
          pos_score = torch.sum(
              seq_output * pos_items_emb, dim=-1
          )  # [B]
          neg_score = torch.sum(
              seq_output * neg_items_emb, dim=-1
          )  # [B]
          loss = self.loss_fct(pos_score, neg_score)
          return loss
      else:  # self.loss_type = 'CE'
          test_item_emb = self.item_embedding.weight
          logits = torch.matmul(
              seq_output, test_item_emb.transpose(0, 1)
          )
          loss = self.loss_fct(logits, pos_items)
          return loss
  
file_path: RecBole/recbole/model/sequential_recommender/stamp.py
imports: import torch
  from torch import nn
  from torch.nn.init import normal_
  from recbole.model.abstract_recommender import SequentialRecommender
  from recbole.model.loss import BPRLoss
label_desc: Use image node_id 5 for calling the STAMP obj's underlying member method code with example usage: obj.calculate_loss(interaction) and returns: loss, loss