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 predict(self, interaction):
      user = interaction[self.USER_ID]
      item = interaction[self.ITEM_ID]
      user_e, item_e = self.forward(user, item)
      return torch.mul(user_e, item_e).sum(dim=1)
  
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 11 for calling the KGNNLS obj's underlying member method code with example usage: obj.predict(interaction) and returns: torch