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 predict(self, interaction):
      item_seq = interaction[self.ITEM_SEQ]
      item_seq_len = interaction[self.ITEM_SEQ_LEN]
      test_item = interaction[self.ITEM_ID]
      seq_output = self.forward(item_seq, bidirectional=False)
      seq_output = self.gather_indexes(seq_output, item_seq_len - 1)
      test_item_emb = self.item_embedding(test_item)
      scores = torch.mul(seq_output, test_item_emb).sum(dim=1)  # [B]
      return scores
  
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 13 for calling the S3Rec obj's underlying member method code with example usage: obj.predict(interaction) and returns: scores