code: def testRegularizationGradient(self, fast_clipping):
      input_dim = 10
      batch_size = 2
      regularizer_multiplier = 0.025
      inputs = tf.keras.layers.Input((input_dim,))
      dense_lyr = tf.keras.layers.Dense(
          1,
          kernel_initializer="ones",
          use_bias=False,
          kernel_regularizer=tf.keras.regularizers.L2(
              regularizer_multiplier
          ),
      )
      # Zero-out outputs to avoid contributions from the main loss function.
      outputs = tf.multiply(dense_lyr(inputs), 0.0)
      model = dp_keras_model.DPModel(
          inputs=inputs,
          outputs=outputs,
          l2_norm_clip=1e9,
          noise_multiplier=0.0,
          layer_registry=layer_registry.make_default_layer_registry()
          if fast_clipping
          else None,
      )
      model.compile(
          loss=tf.keras.losses.MeanSquaredError(),
          optimizer=tf.keras.optimizers.SGD(1.0),
          run_eagerly=True,
      )
      x_batch = tf.reshape(
          tf.range(input_dim * batch_size, dtype=tf.float32),
          [batch_size, input_dim],
      )
      y_batch = tf.zeros([batch_size, 1])
      model.fit(x=x_batch, y=y_batch)
  
      self.assertAllClose(
          model.trainable_variables,
          tf.multiply(
              tf.ones_like(model.trainable_variables),
              1.0 - 2.0 * regularizer_multiplier,
          ),
      )
  
file_path: privacy/tensorflow_privacy/privacy/keras_models/dp_keras_model_test.py
imports: from absl.testing import parameterized
  import numpy
  import tensorflow
  from tensorflow_privacy.privacy.fast_gradient_clipping import layer_registry
  from tensorflow_privacy.privacy.keras_models import dp_keras_model
label_desc: Use image node_id 8 for calling the DPKerasModelTest obj's underlying member method code with example usage: obj.testRegularizationGradient(fast_clipping) without return types