code: def __init__(self, config):
      super(CrossNet, self).__init__()
  
      # load parameters info
      self.depth = config["depth"]
      self.embedding_size = config["embedding_size"]
      self.feature_num = config["feature_num"]
      self.in_feature_num = self.feature_num * self.embedding_size
      self.cross_layer_w = nn.ParameterList(
          nn.Parameter(
              torch.randn(self.in_feature_num, self.in_feature_num)
          )
          for _ in range(self.depth)
      )
      self.bias = nn.ParameterList(
          nn.Parameter(torch.zeros(self.in_feature_num, 1))
          for _ in range(self.depth)
      )
      self.linear = nn.Linear(self.in_feature_num, 1)
      nn.init.normal_(self.linear.weight)
  
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 CrossNet object from inherited base classes: nn with example: obj = CrossNet(config)