code: def __init__(self, args, num_entities, num_relations):
      super(ConvE, 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.hidden_drop = torch.nn.Dropout(args.hidden_drop)
      self.feature_map_drop = torch.nn.Dropout2d(args.feat_drop)
      self.loss = torch.nn.BCELoss()
      self.emb_dim1 = args.embedding_shape1
      self.emb_dim2 = args.embedding_dim // self.emb_dim1
  
      self.conv1 = torch.nn.Conv2d(
          1, 32, (3, 3), 1, 0, bias=args.use_bias
      )
      self.bn0 = torch.nn.BatchNorm2d(1)
      self.bn1 = torch.nn.BatchNorm2d(32)
      self.bn2 = torch.nn.BatchNorm1d(args.embedding_dim)
      self.register_parameter("b", Parameter(torch.zeros(num_entities)))
      self.fc = torch.nn.Linear(args.hidden_size, args.embedding_dim)
      print(num_entities, num_relations)
  
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 ConvE object from inherited base classes: torch.nn with example: obj = ConvE(args, num_entities, num_relations)