class_docstr: KGAT is a knowledge-based recommendation model. It combines knowledge graph and the user-item interaction
  graph to a new graph called collaborative knowledge graph (CKG). This model learns the representations of users and
  items by exploiting the structure of CKG. It adopts a GNN-based architecture and define the attention on the CKG.
class_docstr_tok: ["KGAT","is","a","knowledge-based","recommendation","model",".","It","combines","knowledge","graph","and","the","user-item","interaction","graph","to","a","new","graph","called","collaborative","knowledge","graph","(","CKG",")",".","This","model","learns","the","representations","of","users","and","items","by","exploiting","the","structure","of","CKG",".","It","adopts","a","GNN-based","architecture","and","define","the","attention","on","the","CKG","."]
code: def calculate_loss(self, interaction):
      if (
          self.restore_user_e is not None
          or self.restore_entity_e is not None
      ):
          self.restore_user_e, self.restore_entity_e = None, None
  
      # get loss for training rs
      user = interaction[self.USER_ID]
      pos_item = interaction[self.ITEM_ID]
      neg_item = interaction[self.NEG_ITEM_ID]
  
      user_all_embeddings, entity_all_embeddings = self.forward()
      u_embeddings = user_all_embeddings[user]
      pos_embeddings = entity_all_embeddings[pos_item]
      neg_embeddings = entity_all_embeddings[neg_item]
  
      pos_scores = torch.mul(u_embeddings, pos_embeddings).sum(dim=1)
      neg_scores = torch.mul(u_embeddings, neg_embeddings).sum(dim=1)
      mf_loss = self.mf_loss(pos_scores, neg_scores)
      reg_loss = self.reg_loss(
          u_embeddings, pos_embeddings, neg_embeddings
      )
      loss = mf_loss + self.reg_weight * reg_loss
  
      return loss
  
imports: import numpy
  import scipy.sparse
  import torch
  import torch.nn
  import torch.nn.functional
  from recbole.model.abstract_recommender import KnowledgeRecommender
  from recbole.model.init import xavier_normal_initialization
  from recbole.model.loss import BPRLoss, EmbLoss
  from recbole.utils import InputType
label_desc: Use image node_id 6 for calling the KGAT obj's underlying member method code with example usage: obj.calculate_loss(interaction) and returns: loss