class_docstr: KD_DAGFM is a context-based recommendation model. The model is based on directed acyclic graph and knowledge
  distillation. It can learn arbitrary feature interactions from the complex teacher networks and achieve
  approximately lossless model performance. It can also greatly reduce the computational resource costs.
class_docstr_tok: ["KD_DAGFM","is","a","context-based","recommendation","model",".","The","model","is","based","on","directed","acyclic","graph","and","knowledge","distillation",".","It","can","learn","arbitrary","feature","interactions","from","the","complex","teacher","networks","and","achieve","approximately","lossless","model","performance",".","It","can","also","greatly","reduce","the","computational","resource","costs","."]
code: def FeatureInteraction(self, feature):
      if self.phase == "teacher_training":
          return self.teacher_network.FeatureInteraction(feature)
      elif self.phase == "distillation" or self.phase == "finetuning":
          return self.student_network.FeatureInteraction(feature)
      else:
          return ValueError("Phase invalid!")
  
imports: import torch
  from torch import nn
  from torch.nn.init import xavier_normal_
  from copy import deepcopy
  from recbole.model.init import xavier_normal_initialization
  from recbole.model.abstract_recommender import ContextRecommender
label_desc: Use image node_id 3 for calling the KD_DAGFM obj's underlying member method code with example usage: obj.FeatureInteraction(feature) and returns: self, self, ValueError