code: def __init__(self, config):
      super().__init__()
      self.cinlist = [config["feature_num"]] + config["cin"]
      self.cin = nn.ModuleList(
          [
              CINComp(self.cinlist[i], self.cinlist[i + 1], config)
              for i in range(0, len(self.cinlist) - 1)
          ]
      )
      self.linear = nn.Parameter(
          torch.zeros(sum(self.cinlist) - self.cinlist[0], 1)
      )
      nn.init.normal_(self.linear, mean=0, std=0.01)
      self.backbone = ["cin", "linear"]
      self.loss_fn = nn.BCELoss()
      if torch.cuda.is_available():
          self.device = torch.device("cuda")
      else:
          self.device = torch.device("cpu")
  
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 CIN object from inherited base classes: nn with example: obj = CIN(config)