class_docstr: SASRec is the first sequential recommender based on self-attentive mechanism.
  
  NOTE:
      In the author's implementation, the Point-Wise Feed-Forward Network (PFFN) is implemented
      by CNN with 1x1 kernel. In this implementation, we follows the original BERT implementation
      using Fully Connected Layer to implement the PFFN.
class_docstr_tok: ["SASRec","is","the","first","sequential","recommender","based","on","self-attentive","mechanism",".","NOTE",":","In","the","author","'s","implementation",",","the","Point-Wise","Feed-Forward","Network","(","PFFN",")","is","implemented","by","CNN","with","1x1","kernel",".","In","this","implementation",",","we","follows","the","original","BERT","implementation","using","Fully","Connected","Layer","to","implement","the","PFFN","."]
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/sasrec.py
imports: import torch
  from torch import nn
  from recbole.model.abstract_recommender import SequentialRecommender
  from recbole.model.layers import TransformerEncoder
  from recbole.model.loss import BPRLoss
label_desc: Use image node_id 4 for calling the SASRec obj's underlying member method code with example usage: obj.calculate_loss(interaction) and returns: loss, loss