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 __init__(self, config, dataset):
      super(KD_DAGFM, self).__init__(config, dataset)
  
      # load parameters info
      self.phase = config["phase"]
      self.alpha = config["alpha"]
      self.beta = config["beta"]
  
      # add element to config for the initialization of teacher&student network
      config["feature_num"] = self.num_feature_field
  
      # initialize teacher&student network
      self.student_network = DAGFM(config)
      self.teacher_network = eval(f"{config['teacher']}")(
          self.get_teacher_config(config)
      )
  
      # initialize loss function
      self.loss_fn = nn.BCELoss()
  
      # get warm up parameters
      if self.phase != "teacher_training":
          if "warm_up" not in config:
              raise ValueError("Must have warm up!")
          else:
              save_info = torch.load(config["warm_up"])
              self.load_state_dict(save_info["state_dict"])
      else:
          self.apply(xavier_normal_initialization)
  
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 1 to create a new KD_DAGFM object from inherited base classes: ContextRecommender with example: obj = KD_DAGFM(config, dataset)