class_docstr: DGL Knowledge Embedding Model.
  
  Parameters
  ----------
  args:
      Global configs.
  model_name : str
      Which KG model to use, including 'TransE_l1', 'TransE_l2', 'TransR',
      'RESCAL', 'DistMult', 'ComplEx', 'RotatE', 'SimplE'
  n_entities : int
      Num of entities.
  n_relations : int
      Num of relations.
  hidden_dim : int
      Dimension size of embedding.
  gamma : float
      Gamma for score function.
  double_entity_emb : bool
      If True, entity embedding size will be 2 * hidden_dim.
      Default: False
  double_relation_emb : bool
      If True, relation embedding size will be 2 * hidden_dim.
      Default: False
docstr: Predict the positive score.
  
  Parameters
  ----------
  g : DGLGraph
      Graph holding positive edges.
  
  Returns
  -------
  tensor
      The positive score
docstr_tok: ["Predict","the","positive","score",".","Parameters","--","--","--","--","--","g",":","DGLGraph","Graph","holding","positive","edges",".","Returns","--","--","--","-","tensor","The","positive","score"]
code: def predict_score(self, g):
      """Predict the positive score.
  
      Parameters
      ----------
      g : DGLGraph
          Graph holding positive edges.
  
      Returns
      -------
      tensor
          The positive score
      """
      self.score_func(g)
      return g.edata["score"]
  
imports: import os
  import numpy
  import math
  import dgl.backend
  import pdb
  import torch
  import torch.nn
  from .pytorch.tensor_models import logsigmoid
  from .pytorch.tensor_models import abs
  from .pytorch.tensor_models import masked_select
  from .pytorch.tensor_models import get_device, get_dev
  from .pytorch.tensor_models import norm
  from .pytorch.tensor_models import get_scalar
  from .pytorch.tensor_models import reshape
  from .pytorch.tensor_models import cuda
  from .pytorch.tensor_models import ExternalEmbedding
  from .pytorch.tensor_models import InferEmbedding
  from .pytorch.score_fun import *
  from .pytorch.loss import LossGenerator
label_desc: Use image node_id 6 for calling the KEModel obj's underlying member method code with example usage: obj.predict_score(g) and returns: g