class_docstr: BPR is a basic matrix factorization model that be trained in the pairwise way.
class_docstr_tok: ["BPR","is","a","basic","matrix","factorization","model","that","be","trained","in","the","pairwise","way","."]
code: def full_sort_predict(self, interaction):
      user = interaction[self.USER_ID]
      user_e = self.get_user_embedding(user)
      all_item_e = self.item_embedding.weight
      score = torch.matmul(user_e, all_item_e.transpose(0, 1))
      return score.view(-1)
  
file_path: RecBole/recbole/model/general_recommender/bpr.py
imports: import torch
  import torch.nn
  from recbole.model.abstract_recommender import GeneralRecommender
  from recbole.model.init import xavier_normal_initialization
  from recbole.model.loss import BPRLoss
  from recbole.utils import InputType
label_desc: Use image node_id 7 for calling the BPR obj's underlying member method code with example usage: obj.full_sort_predict(interaction) and returns: score