code: def __init__(self, args, num_entities, num_relations):
      super(Distmult, 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.inp_drop = torch.nn.Dropout(args.input_drop)
      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 Distmult object from inherited base classes: torch.nn with example: obj = Distmult(args, num_entities, num_relations)