code: def FeatureInteraction(self, feature):
      init_state = self.connect_layer @ feature
      h0, ht = init_state, init_state
      state = [torch.sum(init_state, dim=-1)]
      for i in range(self.depth):
          if self.type == "inner":
              aggr = torch.einsum(
                  "bfd,fsd->bsd", ht, self.p[i] * self.adj_matrix
              )
              ht = h0 * aggr
          elif self.type == "outer":
              term = torch.einsum(
                  "bfd,fsd->bfs", ht, self.p[i] * self.adj_matrix
              )
              aggr = torch.einsum("bfs,fsd->bsd", term, self.q[i])
              ht = h0 * aggr
          state.append(torch.sum(ht, dim=-1))
  
      state = torch.cat(state, dim=-1)
      self.logits = self.linear(state)
      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 DAGFM obj's underlying member method code with example usage: obj.FeatureInteraction(feature) and returns: self