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_preferences(self, user_e, item_e, use_st_gumbel=False):
      pref_probs = (
          torch.matmul(
              user_e + item_e,
              torch.t(
                  self.pref_embedding.weight
                  + self.relation_embedding.weight
              ),
          )
          / 2
      )
      if use_st_gumbel:
          # todo: different torch versions may cause the st_gumbel_softmax to report errors, wait to be test
          pref_probs = self.st_gumbel_softmax(pref_probs)
      relation_e = (
          torch.matmul(
              pref_probs,
              self.pref_embedding.weight
              + self.relation_embedding.weight,
          )
          / 2
      )
      norm_e = (
          torch.matmul(
              pref_probs,
              self.pref_norm_embedding.weight
              + self.relation_norm_embedding.weight,
          )
          / 2
      )
      return pref_probs, relation_e, norm_e
  
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 5 for calling the KTUP obj's underlying member method code with example usage: obj._get_preferences(user_e, item_e, use_st_gumbel) and returns: pref_probs,  relation_e,  norm_e