class_docstr: Class corresponding to the regularizer in Algorithm 1 of Kifer et al.
  
  Attributes:
    l2_regularizer: scalar coefficient for l2-regularization term.
    num_train: number of training examples.
    b: tensor of shape (d,num_classes) linearly translating the objective.
class_docstr_tok: ["Class","corresponding","to","the","regularizer","in","Algorithm","1","of","Kifer","et","al",".","Attributes",":","l2_regularizer",":","scalar","coefficient","for","l2-regularization","term",".","num_train",":","number","of","training","examples",".","b",":","tensor","of","shape","(","d",",","num_classes",")","linearly","translating","the","objective","."]
code: def get_config(self):
      return {
          "l2_regularizer": self._l2_regularizer,
          "num_train": self._num_train,
          "b": self._b,
      }
  
file_path: privacy/tensorflow_privacy/privacy/logistic_regression/multinomial_logistic.py
imports: import math
  from typing import List, Optional, Tuple
  import dp_accounting
  import numpy
  import tensorflow
  from tensorflow_privacy.privacy.logistic_regression import datasets
  from tensorflow_privacy.privacy.logistic_regression import single_layer_softmax
  from tensorflow_privacy.privacy.optimizers import dp_optimizer_keras
label_desc: Use image node_id 3 for calling the KiferRegularizer obj's underlying member method code with example usage: obj.get_config() and returns: dict