class_docstr: SASRec is the first sequential recommender based on self-attentive mechanism.
  
  NOTE:
      In the author's implementation, the Point-Wise Feed-Forward Network (PFFN) is implemented
      by CNN with 1x1 kernel. In this implementation, we follows the original BERT implementation
      using Fully Connected Layer to implement the PFFN.
class_docstr_tok: ["SASRec","is","the","first","sequential","recommender","based","on","self-attentive","mechanism",".","NOTE",":","In","the","author","'s","implementation",",","the","Point-Wise","Feed-Forward","Network","(","PFFN",")","is","implemented","by","CNN","with","1x1","kernel",".","In","this","implementation",",","we","follows","the","original","BERT","implementation","using","Fully","Connected","Layer","to","implement","the","PFFN","."]
code: def full_sort_predict(self, interaction):
      loss, prediction_prob = self.calculate_loss_prob(interaction)
      if self.post_remove_context:
          item_seq = interaction[self.ITEM_SEQ]
          prediction_prob.scatter_(1, item_seq, 0)
      return prediction_prob
  
file_path: RecBole/recbole/model/sequential_recommender/sasreccpr.py
imports: import sys
  import torch
  from torch import nn
  import torch.nn.functional
  from recbole.model.abstract_recommender import SequentialRecommender
  from recbole.model.layers import TransformerEncoder
  import math
label_desc: Use image node_id 8 for calling the SASRecCPR obj's underlying member method code with example usage: obj.full_sort_predict(interaction) and returns: prediction_prob