code: def forward(self, X):
      (
          sparse_embedding_list,
          dense_value_list,
      ) = self.input_from_feature_columns(
          X, self.dnn_feature_columns, self.embedding_dict
      )
      logit = self.linear_model(X)
  
      if self.use_fm and len(sparse_embedding_list) > 0:
          fm_input = torch.cat(sparse_embedding_list, dim=1)
          logit += self.fm(fm_input)
  
      if self.use_dnn:
          dnn_input = combined_dnn_input(
              sparse_embedding_list, dense_value_list
          )
          dnn_output = self.dnn(dnn_input)
          dnn_logit = self.dnn_linear(dnn_output)
          logit += dnn_logit
  
      y_pred = self.out(logit)
  
      return y_pred
  
imports: from deepctr_torch.models.deepfm import *
  from deepctr_torch.models.basemodel import BaseModel
label_desc: Use image node_id 2 for calling the DeepFM obj's underlying member method code with example usage: obj.forward(X) and returns: y_pred