class_docstr: Shallow models for knowledge representation learning.
      
docstr: Compute regularization of input embeddings.
          
code: def get_regularization(
      self, h_embed, r_embed, t_embed, neg_embed=None
  ):
      """Compute regularization of input embeddings."""
      if self._args.reg_type == "norm_hrt":
          if (
              self._args.quate_lmbda1 == 0
              and self._args.quate_lmbda2 == 0
          ):
              return 0
          reg_loss = self._score_func.get_hrt_regularization(
              h_embed, r_embed, t_embed, self._args
          )
      else:
          if self._args.reg_coef == 0:
              return 0
          if neg_embed is not None:
              ent_params = paddle.concat(
                  [
                      h_embed,
                      t_embed,
                      neg_embed.reshape((-1, neg_embed.shape[-1])),
                  ],
                  axis=0,
              )
          else:
              ent_params = paddle.concat([h_embed, t_embed], axis=0)
          reg_loss = self._score_func.get_er_regularization(
              ent_params, r_embed, self._args
          )
      return reg_loss
  
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 9 for calling the KGEModel obj's underlying member method code with example usage: obj.get_regularization(h_embed, r_embed, t_embed, neg_embed) and returns: reg_loss, int, int