class_docstr: KTUP is a knowledge-based recommendation model. It adopts the strategy of multi-task learning to jointly learn
  recommendation and KG-related tasks, with the goal of understanding the reasons that a user interacts with an item.
  This method utilizes an attention mechanism to combine all preferences into a single-vector representation.
code: def _get_score(self, h_e, r_e, t_e):
      if self.L1_flag:
          score = -torch.sum(torch.abs(h_e + r_e - t_e), 1)
      else:
          score = -torch.sum((h_e + r_e - t_e) ** 2, 1)
      return score
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.autograd import Variable
  from recbole.model.abstract_recommender import KnowledgeRecommender
  from recbole.model.init import xavier_uniform_initialization
  from recbole.model.loss import BPRLoss, EmbMarginLoss
  from recbole.utils import InputType
label_desc: Use image node_id 7 for calling the KTUP obj's underlying member method code with example usage: obj._get_score(h_e, r_e, t_e) and returns: score