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: Generate pre-training data for the pre-training stage.
docstr_tok: ["Generate","pre-training","data","for","the","pre-training","stage","."]
code: def reconstruct_pretrain_data(self, item_seq, item_seq_len):
      """Generate pre-training data for the pre-training stage."""
      device = item_seq.device
      batch_size = item_seq.size(0)
  
      # We don't need padding for features
      item_feature_seq = (
          self.item_feat[self.FEATURE_FIELD][item_seq.cpu()] - 1
      )
  
      end_index = item_seq_len.cpu().numpy().tolist()
      item_seq = item_seq.cpu().numpy().tolist()
      item_feature_seq = item_feature_seq.cpu().numpy().tolist()
  
      # we will padding zeros at the left side
      # these will be train_instances, after will be reshaped to batch
      sequence_instances = []
      associated_features = (
          []
      )  # For Associated Attribute Prediction and Masked Attribute Prediction
      long_sequence = []
      for i, end_i in enumerate(end_index):
          sequence_instances.append(item_seq[i][:end_i])
          long_sequence.extend(item_seq[i][:end_i])
          # padding feature at the left side
          associated_features.extend(
              [[0] * self.n_features] * (self.max_seq_length - end_i)
          )
          for indexes in item_feature_seq[i][:end_i]:
              features = [0] * self.n_features
              try:
                  # multi class
                  for index in indexes:
                      if index >= 0:
                          features[index] = 1
              except:
                  # single class
                  features[indexes] = 1
              associated_features.append(features)
  
      # Masked Item Prediction and Masked Attribute Prediction
      # [B * Len]
      masked_item_sequence = []
      pos_items = []
      neg_items = []
      for instance in sequence_instances:
          masked_sequence = instance.copy()
          pos_item = instance.copy()
          neg_item = instance.copy()
          for index_id, item in enumerate(instance):
              prob = random.random()
              if prob < self.mask_ratio:
                  masked_sequence[index_id] = self.mask_token
                  neg_item[index_id] = self._neg_sample(instance)
          masked_item_sequence.append(
              self._padding_zero_at_left(masked_sequence)
          )
          pos_items.append(self._padding_zero_at_left(pos_item))
          neg_items.append(self._padding_zero_at_left(neg_item))
  
      # Segment Prediction
      masked_segment_list = []
      pos_segment_list = []
      neg_segment_list = []
      for instance in sequence_instances:
          if len(instance) < 2:
              masked_segment = instance.copy()
              pos_segment = instance.copy()
              neg_segment = instance.copy()
          else:
              sample_length = random.randint(1, len(instance) // 2)
              start_id = random.randint(
                  0, len(instance) - sample_length
              )
              neg_start_id = random.randint(
                  0, len(long_sequence) - sample_length
              )
              pos_segment = instance[
                  start_id : start_id + sample_length
              ]
              neg_segment = long_sequence[
                  neg_start_id : neg_start_id + sample_length
              ]
              masked_segment = (
                  instance[:start_id]
                  + [self.mask_token] * sample_length
                  + instance[start_id + sample_length :]
              )
              pos_segment = (
                  [self.mask_token] * start_id
                  + pos_segment
                  + [self.mask_token]
                  * (len(instance) - (start_id + sample_length))
              )
              neg_segment = (
                  [self.mask_token] * start_id
                  + neg_segment
                  + [self.mask_token]
                  * (len(instance) - (start_id + sample_length))
              )
          masked_segment_list.append(
              self._padding_zero_at_left(masked_segment)
          )
          pos_segment_list.append(
              self._padding_zero_at_left(pos_segment)
          )
          neg_segment_list.append(
              self._padding_zero_at_left(neg_segment)
          )
  
      associated_features = torch.tensor(
          associated_features, dtype=torch.long, device=device
      )
      associated_features = associated_features.view(
          -1, self.max_seq_length, self.n_features
      )
  
      masked_item_sequence = torch.tensor(
          masked_item_sequence, dtype=torch.long, device=device
      ).view(batch_size, -1)
      pos_items = torch.tensor(
          pos_items, dtype=torch.long, device=device
      ).view(batch_size, -1)
      neg_items = torch.tensor(
          neg_items, dtype=torch.long, device=device
      ).view(batch_size, -1)
      masked_segment_list = torch.tensor(
          masked_segment_list, dtype=torch.long, device=device
      ).view(batch_size, -1)
      pos_segment_list = torch.tensor(
          pos_segment_list, dtype=torch.long, device=device
      ).view(batch_size, -1)
      neg_segment_list = torch.tensor(
          neg_segment_list, dtype=torch.long, device=device
      ).view(batch_size, -1)
  
      return (
          associated_features,
          masked_item_sequence,
          pos_items,
          neg_items,
          masked_segment_list,
          pos_segment_list,
          neg_segment_list,
      )
  
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 11 for calling the S3Rec obj's underlying member method code with example usage: obj.reconstruct_pretrain_data(item_seq, item_seq_len) and returns: associated_features,  masked_item_sequence,  pos_items,  neg_items,  masked_segment_list,  pos_segment_list,  neg_segment_list