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.
docstr: Pretrain out model using four pre-training tasks:
  
  1. Associated Attribute Prediction
  
  2. Masked Item Prediction
  
  3. Masked Attribute Prediction
  
  4. Segment Prediction
docstr_tok: ["Pretrain","out","model","using","four","pre-training","tasks",":","1",".","Associated","Attribute","Prediction","2",".","Masked","Item","Prediction","3",".","Masked","Attribute","Prediction","4",".","Segment","Prediction"]
code: def pretrain(
      self,
      features,
      masked_item_sequence,
      pos_items,
      neg_items,
      masked_segment_sequence,
      pos_segment,
      neg_segment,
  ):
      """Pretrain out model using four pre-training tasks:
  
      1. Associated Attribute Prediction
  
      2. Masked Item Prediction
  
      3. Masked Attribute Prediction
  
      4. Segment Prediction
      """
      # Encode masked sequence
      sequence_output = self.forward(masked_item_sequence)
  
      feature_embedding = self.feature_embedding.weight
      # AAP
      aap_score = self._associated_attribute_prediction(
          sequence_output, feature_embedding
      )
      aap_loss = self.loss_fct(
          aap_score, features.view(-1, self.n_features).float()
      )
      # only compute loss at non-masked position
      aap_mask = (masked_item_sequence != self.mask_token).float() * (
          masked_item_sequence != 0
      ).float()
      aap_loss = torch.sum(aap_loss * aap_mask.flatten().unsqueeze(-1))
  
      # MIP
      pos_item_embs = self.item_embedding(pos_items)
      neg_item_embs = self.item_embedding(neg_items)
      pos_score = self._masked_item_prediction(
          sequence_output, pos_item_embs
      )
      neg_score = self._masked_item_prediction(
          sequence_output, neg_item_embs
      )
      mip_distance = pos_score - neg_score
      mip_loss = self.loss_fct(
          mip_distance,
          torch.ones_like(mip_distance, dtype=torch.float32),
      )
      mip_mask = (masked_item_sequence == self.mask_token).float()
      mip_loss = torch.sum(mip_loss * mip_mask.flatten())
  
      # MAP
      map_score = self._masked_attribute_prediction(
          sequence_output, feature_embedding
      )
      map_loss = self.loss_fct(
          map_score, features.view(-1, self.n_features).float()
      )
      map_mask = (masked_item_sequence == self.mask_token).float()
      map_loss = torch.sum(map_loss * map_mask.flatten().unsqueeze(-1))
  
      # SP
      # segment context
      # take the last position hidden as the context
      segment_context = self.forward(masked_segment_sequence)[
          :, -1, :
      ]  # [B H]
      pos_segment_emb = self.forward(pos_segment)[:, -1, :]
      neg_segment_emb = self.forward(neg_segment)[:, -1, :]  # [B H]
      pos_segment_score = self._segment_prediction(
          segment_context, pos_segment_emb
      )
      neg_segment_score = self._segment_prediction(
          segment_context, neg_segment_emb
      )
      sp_distance = pos_segment_score - neg_segment_score
      sp_loss = torch.sum(
          self.loss_fct(
              sp_distance,
              torch.ones_like(sp_distance, dtype=torch.float32),
          )
      )
  
      pretrain_loss = (
          self.aap_weight * aap_loss
          + self.mip_weight * mip_loss
          + self.map_weight * map_loss
          + self.sp_weight * sp_loss
      )
  
      return pretrain_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 8 for calling the S3Rec obj's underlying member method code with example usage: obj.pretrain(features, masked_item_sequence, pos_items, neg_items, masked_segment_sequence, pos_segment, neg_segment) and returns: pretrain_loss