code: def testNoiseMultiplier(self, cls):
      with tf.GradientTape(persistent=True) as gradient_tape:
          var0 = tf.Variable([0.0])
          data0 = tf.Variable([[0.0]])
  
          dp_sum_query = gaussian_query.GaussianSumQuery(4.0, 8.0)
  
          opt = cls(dp_sum_query, num_microbatches=1, learning_rate=2.0)
  
          self.evaluate(tf.compat.v1.global_variables_initializer())
          # Fetch params to validate initial values
          self.assertAllClose([0.0], self.evaluate(var0))
  
          grads = []
          for _ in range(1000):
              grads_and_vars = opt.compute_gradients(
                  lambda: self._loss_fn(var0, data0),
                  [var0],
                  gradient_tape=gradient_tape,
              )
              grads.append(grads_and_vars[0][0])
  
          # Test standard deviation is close to l2_norm_clip * noise_multiplier.
          self.assertNear(np.std(grads), 2.0 * 4.0, 0.5)
  
file_path: privacy/tensorflow_privacy/privacy/optimizers/dp_optimizer_eager_test.py
imports: from absl.testing import parameterized
  import numpy
  import tensorflow
  from tensorflow_privacy.privacy.dp_query import gaussian_query
  from tensorflow_privacy.privacy.optimizers import dp_optimizer
label_desc: Use image node_id 5 for calling the DPOptimizerEagerTest obj's underlying member method code with example usage: obj.testNoiseMultiplier(cls) without return types