code: def forward(self, e1, rel, kg_graph=None):
      X = torch.LongTensor(list(range(self.num_entities))).to(e1.device)
  
      kg_graph.node_features["node_feat"] = self.emb_e_real(X)
      kg_graph = self.gnn(kg_graph)
      e1_embedded_real = kg_graph.node_features["node_feat"][
          e1
      ].squeeze(1)
  
      kg_graph.node_features["node_feat"] = self.emb_e_img(X)
      kg_graph = self.gnn(kg_graph)
      e1_embedded_img = kg_graph.node_features["node_feat"][e1].squeeze(
          1
      )
  
      rel_embedded_real = self.emb_rel_real(rel).squeeze(1)
      rel_embedded_img = self.emb_rel_img(rel).squeeze(1)
  
      kg_graph = self.complex(
          kg_graph,
          e1_embedded_real,
          rel_embedded_real,
          e1_embedded_img,
          rel_embedded_img,
          self.emb_e_real,
          self.emb_e_img,
      )
      logits = kg_graph.graph_attributes["logits"]
  
      return logits
  
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 GCNComplex obj's underlying member method code with example usage: obj.forward(e1, rel, kg_graph) and returns: logits