class_docstr: S3Rec is the first work to incorporate self-supervised learning in
  sequential recommendation.
  
  NOTE:
      Under this framework, we need reconstruct the pretraining data,
      which would affect the pre-training speed.
code: def calculate_loss(self, interaction):
      item_seq = interaction[self.ITEM_SEQ]
      item_seq_len = interaction[self.ITEM_SEQ_LEN]
      # pretrain
      if self.train_stage == "pretrain":
          (
              features,
              masked_item_sequence,
              pos_items,
              neg_items,
              masked_segment_sequence,
              pos_segment,
              neg_segment,
          ) = self.reconstruct_pretrain_data(item_seq, item_seq_len)
  
          loss = self.pretrain(
              features,
              masked_item_sequence,
              pos_items,
              neg_items,
              masked_segment_sequence,
              pos_segment,
              neg_segment,
          )
      # finetune
      else:
          pos_items = interaction[self.POS_ITEM_ID]
          # we use uni-directional attention in the fine-tuning stage
          seq_output = self.forward(item_seq, bidirectional=False)
          seq_output = self.gather_indexes(seq_output, item_seq_len - 1)
  
          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)
          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
  
imports: import random
  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 12 for calling the S3Rec obj's underlying member method code with example usage: obj.calculate_loss(interaction) and returns: loss