code: def __init__(self, config):
      super(DAGFM, self).__init__()
      if torch.cuda.is_available():
          self.device = torch.device("cuda")
      else:
          self.device = torch.device("cpu")
  
      # load parameters info
      self.type = config["type"]
      self.depth = config["depth"]
      field_num = config["feature_num"]
      embedding_size = config["embedding_size"]
  
      # initialize parameters according to the type
      if self.type == "inner":
          self.p = nn.ParameterList(
              [
                  nn.Parameter(
                      torch.randn(field_num, field_num, embedding_size)
                  )
                  for _ in range(self.depth)
              ]
          )
          for _ in range(self.depth):
              xavier_normal_(self.p[_], gain=1.414)
      elif self.type == "outer":
          self.p = nn.ParameterList(
              [
                  nn.Parameter(
                      torch.randn(field_num, field_num, embedding_size)
                  )
                  for _ in range(self.depth)
              ]
          )
          self.q = nn.ParameterList(
              [
                  nn.Parameter(
                      torch.randn(field_num, field_num, embedding_size)
                  )
                  for _ in range(self.depth)
              ]
          )
          for _ in range(self.depth):
              xavier_normal_(self.p[_], gain=1.414)
              xavier_normal_(self.q[_], gain=1.414)
      self.adj_matrix = torch.zeros(
          field_num, field_num, embedding_size
      ).to(self.device)
      for i in range(field_num):
          for j in range(i, field_num):
              self.adj_matrix[i, j, :] += 1
      self.connect_layer = nn.Parameter(torch.eye(field_num).float())
      self.linear = nn.Linear(field_num * (self.depth + 1), 1)
  
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 DAGFM object from inherited base classes: nn with example: obj = DAGFM(config)