class_docstr: Test loss function for testing BoltOn model.
class_docstr_tok: ["Test","loss","function","for","testing","BoltOn","model","."]
docstr: Returns the kernel_regularizer to be used.
  
  Any subclass should override this method if they want a kernel_regularizer
  (if required for the loss function to be StronglyConvex.
docstr_tok: ["Returns","the","kernel_regularizer","to","be","used",".","Any","subclass","should","override","this","method","if","they","want","a","kernel_regularizer","(","if","required","for","the","loss","function","to","be","StronglyConvex","."]
code: def kernel_regularizer(self):
      """Returns the kernel_regularizer to be used.
  
      Any subclass should override this method if they want a kernel_regularizer
      (if required for the loss function to be StronglyConvex.
      """
      return L1L2(l2=self.reg_lambda)
  
imports: import unittest
  from absl.testing import parameterized
  import tensorflow.compat.v1
  from tensorflow.compat.v1.python import ops
  from tensorflow.compat.v1.python.framework import test_util
  from tensorflow.compat.v1.python.keras import keras_parameterized
  from tensorflow.compat.v1.python.keras import losses
  from tensorflow.compat.v1.python.keras.initializers import constant
  from tensorflow.compat.v1.python.keras.models import Model
  from tensorflow.compat.v1.python.keras.optimizer_v2.optimizer_v2 import OptimizerV2
  from tensorflow.compat.v1.python.keras.regularizers import L1L2
  from tensorflow.compat.v1.python.platform import test
  from tensorflow_privacy.privacy.bolt_on import optimizers
  from tensorflow_privacy.privacy.bolt_on.losses import StrongConvexMixin
label_desc: Use image node_id 8 for calling the TestLoss obj's underlying member method code with example usage: obj.kernel_regularizer() and returns: L1L2