class_docstr: Base class for models
class_docstr_tok: ["Base","class","for","models"]
docstr: Construct L1-norm and L2-norm on cross network parameters for loss function.
  
  Returns:
      object: Regular loss value on cross network parameters.
docstr_tok: ["Construct","L1-norm","and","L2-norm","on","cross","network","parameters","for","loss","function",".","Returns",":","object",":","Regular","loss","value","on","cross","network","parameters","."]
code: def _cross_l_loss(self):
      """Construct L1-norm and L2-norm on cross network parameters for loss function.
  
      Returns:
          object: Regular loss value on cross network parameters.
      """
      cross_l_loss = tf.zeros([1], dtype=tf.float32)
      for param in self.cross_params:
          cross_l_loss = tf.add(
              cross_l_loss,
              tf.multiply(
                  self.hparams.cross_l1, tf.norm(tensor=param, ord=1)
              ),
          )
          cross_l_loss = tf.add(
              cross_l_loss,
              tf.multiply(
                  self.hparams.cross_l2, tf.norm(tensor=param, ord=2)
              ),
          )
      return cross_l_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 8 for calling the BaseModel obj's underlying member method code with example usage: obj._cross_l_loss() and returns: cross_l_loss