code: def __init__(self, args, num_entities, num_relations, num_layers=2):
      super(GCNDistMult, self).__init__()
      self.emb_e = torch.nn.Embedding(
          num_entities, args.embedding_dim, padding_idx=0
      )
      self.emb_rel = torch.nn.Embedding(
          num_relations, args.embedding_dim, padding_idx=0
      )
  
      self.num_entities = num_entities
      self.num_relations = num_relations
  
      self.num_layers = num_layers
      self.gnn = GCN(
          self.num_layers,
          args.embedding_dim,
          args.embedding_dim,
          args.embedding_dim,
          args.direction_option,
          feat_drop=args.input_drop,
      )
  
      self.direction_option = args.direction_option
      self.distmult = DistMult(args.input_drop, loss_name="BCELoss")
      self.loss = torch.nn.BCELoss()
  
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 1 to create a new GCNDistMult object from inherited base classes: torch.nn with example: obj = GCNDistMult(args, num_entities, num_relations, num_layers)