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 calculate_loss(self, interaction):
      user = interaction[self.USER_ID]
      pos_item = interaction[self.ITEM_ID]
      neg_item = interaction[self.NEG_ITEM_ID]
      proj_pos_user_e, pos_relation_e, proj_pos_item_e = self.forward(
          user, pos_item
      )
      proj_neg_user_e, neg_relation_e, proj_neg_item_e = self.forward(
          user, neg_item
      )
  
      pos_item_score = self._get_score(
          proj_pos_user_e, pos_relation_e, proj_pos_item_e
      )
      neg_item_score = self._get_score(
          proj_neg_user_e, neg_relation_e, proj_neg_item_e
      )
  
      rec_loss = self.rec_loss(pos_item_score, neg_item_score)
      orthogonal_loss = orthogonalLoss(
          self.pref_embedding.weight, self.pref_norm_embedding.weight
      )
      item = torch.cat([pos_item, neg_item])
      align_loss = self.align_weight * alignLoss(
          self.item_embedding(item),
          self.entity_embedding(item),
          self.L1_flag,
      )
  
      return rec_loss, orthogonal_loss, align_loss
  
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 9 for calling the KTUP obj's underlying member method code with example usage: obj.calculate_loss(interaction) and returns: rec_loss,  orthogonal_loss,  align_loss