code: def loss(self, node_embedding, triplets, labels):
      _, _, _, _, torch, F, _ = lazy_import_networks()
      score = self.score(node_embedding, triplets)
  
      # binary crossentropy loss
      ce_loss = F.binary_cross_entropy_with_logits(score, labels)
  
      # regularization loss
      ne_ = torch.mean(node_embedding.pow(2))  # type: ignore
      re_ = torch.mean(self.relational_embedding.pow(2))
      rl = ne_ + re_
  
      return ce_loss + self.reg * rl
  
imports: from typing import TYPE_CHECKING, Any
  from .None import constants
  import logging
label_desc: Use image node_id 4 for calling the HeteroEmbed obj's underlying member method code with example usage: obj.loss(node_embedding, triplets, labels) and returns: unknown