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.
docstr: Calculate the training loss for a batch data of KG.
  
  Args:
      interaction (Interaction): Interaction class of the batch.
  
  Returns:
      torch.Tensor: Training loss, shape: []
docstr_tok: ["Calculate","the","training","loss","for","a","batch","data","of","KG",".","Args",":","interaction","(","Interaction",")",":","Interaction","class","of","the","batch",".","Returns",":","torch.Tensor",":","Training","loss",",","shape",":","[","]"]
code: def calculate_kg_loss(self, interaction):
      r"""Calculate the training loss for a batch data of KG.
  
      Args:
          interaction (Interaction): Interaction class of the batch.
  
      Returns:
          torch.Tensor: Training loss, shape: []
      """
  
      h = interaction[self.HEAD_ENTITY_ID]
      r = interaction[self.RELATION_ID]
      pos_t = interaction[self.TAIL_ENTITY_ID]
      neg_t = interaction[self.NEG_TAIL_ENTITY_ID]
  
      h_e = self.entity_embedding(h)
      pos_t_e = self.entity_embedding(pos_t)
      neg_t_e = self.entity_embedding(neg_t)
      r_e = self.relation_embedding(r)
      norm_e = self.relation_norm_embedding(r)
  
      proj_h_e = self._transH_projection(h_e, norm_e)
      proj_pos_t_e = self._transH_projection(pos_t_e, norm_e)
      proj_neg_t_e = self._transH_projection(neg_t_e, norm_e)
  
      pos_tail_score = self._get_score(proj_h_e, r_e, proj_pos_t_e)
      neg_tail_score = self._get_score(proj_h_e, r_e, proj_neg_t_e)
  
      kg_loss = self.kg_loss(
          pos_tail_score,
          neg_tail_score,
          torch.ones(h.size(0)).to(self.device),
      )
      orthogonal_loss = orthogonalLoss(r_e, norm_e)
      reg_loss = self.reg_loss(h_e, pos_t_e, neg_t_e, r_e)
      loss = self.kg_weight * (kg_loss + orthogonal_loss + reg_loss)
      entity = torch.cat([h, pos_t, neg_t])
      entity = entity[entity < self.n_items]
      align_loss = self.align_weight * alignLoss(
          self.item_embedding(entity),
          self.entity_embedding(entity),
          self.L1_flag,
      )
  
      return 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 10 for calling the KTUP obj's underlying member method code with example usage: obj.calculate_kg_loss(interaction) and returns: loss,  align_loss