class_docstr: MKR is a Multi-task feature learning approach for Knowledge graph enhanced Recommendation. It is a deep
  end-to-end framework that utilizes knowledge graph embedding task to assist recommendation task. The two
  tasks are associated by cross&compress units, which automatically share latent features and learn high-order
  interactions between items in recommender systems and entities in the knowledge graph.
class_docstr_tok: ["MKR","is","a","Multi-task","feature","learning","approach","for","Knowledge","graph","enhanced","Recommendation",".","It","is","a","deep","end-to-end","framework","that","utilizes","knowledge","graph","embedding","task","to","assist","recommendation","task",".","The","two","tasks","are","associated","by","cross","&","compress","units",",","which","automatically","share","latent","features","and","learn","high-order","interactions","between","items","in","recommender","systems","and","entities","in","the","knowledge","graph","."]
code: def predict(self, interaction):
      user = interaction[self.USER_ID]
      item = interaction[self.ITEM_ID]
      head = interaction[self.ITEM_ID]
  
      outputs = self.forward(user, item, head)
      _, _, scores, _ = outputs
  
      return scores
  
file_path: RecBole/recbole/model/knowledge_aware_recommender/mkr.py
imports: import torch
  import torch.nn
  from recbole.model.abstract_recommender import KnowledgeRecommender
  from recbole.model.init import xavier_normal_initialization
  from recbole.model.layers import MLPLayers
  from recbole.utils import InputType
label_desc: Use image node_id 6 for calling the MKR obj's underlying member method code with example usage: obj.predict(interaction) and returns: scores