code: def get_loss(self, embed, triplets, labels):
      # each row in the triplets is a 3-tuple of (source, relation, destination)
      score = self.calc_score(embed, triplets)
      predict_loss = F.binary_cross_entropy_with_logits(score, labels)
      reg_loss = self.regularization_loss(embed)
      return predict_loss + self.reg_param * reg_loss
  
imports: import dgl
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import tqdm
  from dgl.data.knowledge_graph import FB15k237Dataset
  from dgl.dataloading import GraphDataLoader
  from dgl.nn.pytorch import RelGraphConv
label_desc: Use image node_id 5 for calling the LinkPredict obj's underlying member method code with example usage: obj.get_loss(embed, triplets, labels) and returns: unknown