class_docstr: loss generator class is responsible for calculate loss for positive & negative loss / pairwise loss.
  It has different implementations of concrete method in regards of PyTorch and MXNet.
docstr: Predict loss for negative triples
  
  Parameters
  ----------
  neg_score: tensor
      Score calculated from positive triples
  edge_weight : tensor
      weight for each edge
  
  Returns
  -------
  tensor
      Negative loss calculated with specific loss criterion
docstr_tok: ["Predict","loss","for","negative","triples","Parameters","--","--","--","--","--","neg_score",":","tensor","Score","calculated","from","positive","triples","edge_weight",":","tensor","weight","for","each","edge","Returns","--","--","--","-","tensor","Negative","loss","calculated","with","specific","loss","criterion"]
code: def get_neg_loss(self, neg_score, edge_weight):
      """Predict loss for negative triples
  
      Parameters
      ----------
      neg_score: tensor
          Score calculated from positive triples
      edge_weight : tensor
          weight for each edge
  
      Returns
      -------
      tensor
          Negative loss calculated with specific loss criterion
      """
      pass
  
label_desc: Use image node_id 3 for calling the BaseLossGenerator obj's underlying member method code with example usage: obj.get_neg_loss(neg_score, edge_weight) without return types