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 _masked_item_prediction(self, sequence_output, target_item_emb):
      sequence_output = self.mip_norm(
          sequence_output.view([-1, sequence_output.size(-1)])
      )  # [B*L H]
      target_item_emb = target_item_emb.view(
          [-1, sequence_output.size(-1)]
      )  # [B*L H]
      score = torch.mul(sequence_output, target_item_emb)  # [B*L H]
      return torch.sigmoid(torch.sum(score, -1))
  
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 4 for calling the S3Rec obj's underlying member method code with example usage: obj._masked_item_prediction(sequence_output, target_item_emb) and returns: torch