code: def FeatureInteraction(self, feature):
      base = feature
      x = feature
      p = []
      for comp in self.cin:
          x = comp(x, base)
          p.append(torch.sum(x, dim=-1))
      p = torch.cat(p, dim=-1)
      self.logits = p @ self.linear
      self.outputs = torch.sigmoid(self.logits)
      return self.outputs
  
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 2 for calling the CIN obj's underlying member method code with example usage: obj.FeatureInteraction(feature) and returns: self