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: Load the model.
  
  Parameters
  ----------
  path : str
      Directory to load the model.
  dataset : str
      Dataset name as prefix to the saved embeddings.
docstr_tok: ["Load","the","model",".","Parameters","--","--","--","--","--","path",":","str","Directory","to","load","the","model",".","dataset",":","str","Dataset","name","as","prefix","to","the","saved","embeddings","."]
code: def load_emb(self, path, dataset):
      """Load the model.
  
      Parameters
      ----------
      path : str
          Directory to load the model.
      dataset : str
          Dataset name as prefix to the saved embeddings.
      """
      self.entity_emb.load(
          path, dataset + "_" + self.model_name + "_entity"
      )
      self.relation_emb.load(
          path, dataset + "_" + self.model_name + "_relation"
      )
      self.score_func.load(path, dataset + "_" + self.model_name)
  
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 4 for calling the KEModel obj's underlying member method code with example usage: obj.load_emb(path, dataset) without return types