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 full_sort_predict(self, interaction):
      user = interaction[self.USER_ID]
      if self.restore_user_e is None or self.restore_entity_e is None:
          self.restore_user_e, self.restore_entity_e, _ = self.forward()
      u_embeddings = self.restore_user_e[user]
      i_embeddings = self.restore_entity_e[: self.n_items]
  
      scores = torch.matmul(u_embeddings, i_embeddings.transpose(0, 1))
  
      return scores.view(-1)
  
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 7 for calling the KGIN obj's underlying member method code with example usage: obj.full_sort_predict(interaction) and returns: scores