class_docstr: Base class for sequential models
class_docstr_tok: ["Base","class","for","sequential","models"]
docstr: Regularization for embedding variables and other variables.
docstr_tok: ["Regularization","for","embedding","variables","and","other","variables","."]
code: def _add_norm(self):
      """Regularization for embedding variables and other variables."""
      all_variables, embed_variables = (
          tf.compat.v1.trainable_variables(),
          tf.compat.v1.trainable_variables(
              self.sequential_scope._name + "/embedding"
          ),
      )
      layer_params = list(set(all_variables) - set(embed_variables))
      layer_params = [
          a for a in layer_params if "_no_reg" not in a.name
      ]
      self.layer_params.extend(layer_params)
  
file_path: recommenders/recommenders/models/deeprec/models/sequential/sequential_base_model.py
imports: import os
  import abc
  import numpy
  import tensorflow
  from recommenders.models.deeprec.models.base_model import BaseModel
  from recommenders.models.deeprec.deeprec_utils import cal_metric, load_dict
label_desc: Use image node_id 9 for calling the SequentialBaseModel obj's underlying member method code with example usage: obj._add_norm() without return types