class_docstr: Shallow models for knowledge representation learning.
      
code: def _init_embedding(self):
      if self._model_name == "quate":
          ent_weight = self._init_func(
              "quaternion_init", self._num_ents, self._ent_dim
          )
          rel_weight = self._init_func(
              "quaternion_init", self._num_rels, self._rel_dim
          )
      elif self._model_name == "ote":
          ent_weight = self._init_func(
              "ote_entity_uniform", self._num_ents, self._ent_dim
          )
          rel_weight = self._init_func(
              "ote_scale_init", self._num_rels, self._rel_dim
          )
      else:
          ent_weight = self._init_func(
              "general_uniform", self._num_ents, self._ent_dim
          )
          rel_weight = self._init_func(
              "general_uniform", self._num_rels, self._rel_dim
          )
  
      if self._ent_emb_on_cpu:
          assert (
              self._ent_weight_path is not None
          ), "Entity embedding path is not given!"
          ent_embeds = SharedEmbedding.from_array(
              weight=ent_weight,
              weight_path=self._ent_weight_path,
              optimizer=self._optim,
              learning_rate=self._lr,
              num_workers=self._args.num_process,
          )
      else:
          ent_embeds = nn.Embedding(self._num_ents, self._ent_dim)
          ent_embeds.weight.set_value(ent_weight)
  
      if self._rel_emb_on_cpu:
          assert (
              self._rel_weight_path is not None
          ), "Relation embedding path is not given!"
          rel_embeds = SharedEmbedding.from_array(
              weight=rel_weight,
              weight_path=self._rel_weight_path,
              optimizer=self._optim,
              learning_rate=self._lr,
              num_workers=self._args.num_process,
          )
      else:
          rel_embeds = nn.Embedding(self._num_rels, self._rel_dim)
          rel_embeds.weight.set_value(rel_weight)
  
      return ent_embeds, rel_embeds
  
imports: import os
  import json
  import math
  import warnings
  import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from pgl.utils.shared_embedding import SharedEmbedding
  from models.score_funcs import TransEScore, RotatEScore, DistMultScore, ComplExScore, QuatEScore, OTEScore
  from models.init_func import InitFunction
  from utils import uniform, timer_wrapper
label_desc: Use image node_id 18 for calling the KGEModel obj's underlying member method code with example usage: obj._init_embedding() and returns: ent_embeds,  rel_embeds