class_docstr: KGIN is a knowledge-aware recommendation model. It combines knowledge graph and the user-item interaction
  graph to a new graph called collaborative knowledge graph (CKG). This model explores intents behind a user-item
  interaction by using auxiliary item knowledge.
class_docstr_tok: ["KGIN","is","a","knowledge-aware","recommendation","model",".","It","combines","knowledge","graph","and","the","user-item","interaction","graph","to","a","new","graph","called","collaborative","knowledge","graph","(","CKG",")",".","This","model","explores","intents","behind","a","user-item","interaction","by","using","auxiliary","item","knowledge","."]
code: def predict(self, interaction):
      user = interaction[self.USER_ID]
      item = interaction[self.ITEM_ID]
  
      user_all_embeddings, entity_all_embeddings, _ = self.forward()
  
      u_embeddings = user_all_embeddings[user]
      i_embeddings = entity_all_embeddings[item]
      scores = torch.mul(u_embeddings, i_embeddings).sum(dim=1)
      return scores
  
imports: import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import scipy.sparse
  from recbole.model.abstract_recommender import KnowledgeRecommender
  from recbole.model.init import xavier_uniform_initialization
  from recbole.model.layers import SparseDropout
  from recbole.model.loss import BPRLoss, EmbLoss
  from recbole.utils import InputType
label_desc: Use image node_id 6 for calling the KGIN obj's underlying member method code with example usage: obj.predict(interaction) and returns: scores