class_docstr: Base class for models
class_docstr_tok: ["Base","class","for","models"]
docstr: Construct regular loss. Usually it's comprised of l1 and l2 norm.
  Users can designate which norm to be included via config file.
  
  Returns:
      object: Regular loss.
docstr_tok: ["Construct","regular","loss",".","Usually","it","'s","comprised","of","l1","and","l2","norm",".","Users","can","designate","which","norm","to","be","included","via","config","file",".","Returns",":","object",":","Regular","loss","."]
code: def _compute_regular_loss(self):
      """Construct regular loss. Usually it's comprised of l1 and l2 norm.
      Users can designate which norm to be included via config file.
  
      Returns:
          object: Regular loss.
      """
      regular_loss = (
          self._l2_loss() + self._l1_loss() + self._cross_l_loss()
      )
      return tf.reduce_sum(input_tensor=regular_loss)
  
imports: from os.path import join
  import abc
  import time
  import os
  import numpy
  import tensorflow
  from recommenders.models.deeprec.deeprec_utils import cal_metric
label_desc: Use image node_id 11 for calling the BaseModel obj's underlying member method code with example usage: obj._compute_regular_loss() and returns: tf