code: def FeatureInteraction(self, x_0):
      x_0 = x_0.reshape(x_0.shape[0], -1)
      x_0 = x_0.unsqueeze(dim=2)
      x_l = x_0  # (batch_size, in_feature_num, 1)
      for i in range(self.depth):
          xl_w = torch.matmul(self.cross_layer_w[i], x_l)
          xl_w = xl_w + self.bias[i]
          xl_dot = torch.mul(x_0, xl_w)
          x_l = xl_dot + x_l
      x_l = x_l.squeeze(dim=2)
      self.logits = self.linear(x_l)
      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 CrossNet obj's underlying member method code with example usage: obj.FeatureInteraction(x_0) and returns: self