class_docstr: KGNN-LS is a knowledge-based recommendation model.
  KGNN-LS transforms the knowledge graph into a user-specific weighted graph and then apply a graph neural network to
  compute personalized item embeddings. To provide better inductive bias, KGNN-LS relies on label smoothness
  assumption, which posits that adjacent items in the knowledge graph are likely to have similar user relevance
  labels/scores. Label smoothness provides regularization over the edge weights and it is equivalent  to a label
  propagation scheme on a graph.
code: def calculate_loss(self, interaction):
      user = interaction[self.USER_ID]
      pos_item = interaction[self.ITEM_ID]
      neg_item = interaction[self.NEG_ITEM_ID]
      target = torch.zeros(len(user) * 2, dtype=torch.float32).to(
          self.device
      )
      target[: len(user)] = 1
  
      users = torch.cat((user, user))
      items = torch.cat((pos_item, neg_item))
  
      user_e, item_e = self.forward(users, items)
      predict = torch.mul(user_e, item_e).sum(dim=1)
      rec_loss = self.bce_loss(predict, target)
  
      ls_loss = self.calculate_ls_loss(users, items, target)
      l2_loss = self.l2_loss(user_e, item_e)
  
      loss = (
          rec_loss
          + self.ls_weight * ls_loss
          + self.reg_weight * l2_loss
      )
      return loss
  
imports: import random
  import numpy
  import torch
  import torch.nn
  from recbole.model.abstract_recommender import KnowledgeRecommender
  from recbole.model.init import xavier_normal_initialization
  from recbole.model.loss import EmbLoss
  from recbole.utils import InputType
label_desc: Use image node_id 10 for calling the KGNNLS obj's underlying member method code with example usage: obj.calculate_loss(interaction) and returns: loss