code: def forward(self, e1, rel, kg_graph=None):
      e1_embedded = self.emb_e(e1).view(
          -1, 1, self.emb_dim1, self.emb_dim2
      )
      rel_embedded = self.emb_rel(rel).view(
          -1, 1, self.emb_dim1, self.emb_dim2
      )
  
      stacked_inputs = torch.cat([e1_embedded, rel_embedded], 2)
  
      stacked_inputs = self.bn0(stacked_inputs)
      x = self.inp_drop(stacked_inputs)
      x = self.conv1(x)
      x = self.bn1(x)
      x = F.relu(x)
      x = self.feature_map_drop(x)
      x = x.view(x.shape[0], -1)
      x = self.fc(x)
      x = self.hidden_drop(x)
      x = self.bn2(x)
      x = F.relu(x)
      x = torch.mm(x, self.emb_e.weight.transpose(1, 0))
      x += self.b.expand_as(x)
      pred = torch.sigmoid(x)
  
      return pred
  
imports: import torch
  from torch.nn import Parameter
  from torch.nn import functional
  from torch.nn.init import xavier_normal_
  from graph4nlp.pytorch.modules.graph_embedding_learning.gcn import GCN
  from graph4nlp.pytorch.modules.graph_embedding_learning.ggnn import GGNN
  from graph4nlp.pytorch.modules.prediction.classification.kg_completion import ComplEx, DistMult
label_desc: Use image node_id 3 for calling the ConvE obj's underlying member method code with example usage: obj.forward(e1, rel, kg_graph) and returns: pred