code: def testNoiseMultiplier(self, cls, num_microbatches):
      with self.cached_session() as sess:
          var0 = tf.Variable([0.0])
          data0 = tf.Variable([[0.0], [0.0], [0.0], [0.0]])
  
          dp_sum_query = gaussian_query.GaussianSumQuery(4.0, 8.0)
  
          opt = cls(
              dp_sum_query,
              num_microbatches=num_microbatches,
              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))
  
          gradient_op = opt.compute_gradients(
              self._loss(data0, var0), [var0]
          )
          grads = []
          for _ in range(1000):
              grads_and_vars = sess.run(gradient_op)
              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 / num_microbatches, 0.5
          )
  
imports: import os
  import unittest
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from tensorflow import estimator
  from tensorflow.compat.v1 import estimator
  from tensorflow_privacy.privacy.dp_query import gaussian_query
  from tensorflow_privacy.privacy.optimizers import dp_optimizer
label_desc: Use image node_id 7 for calling the DPOptimizerTest obj's underlying member method code with example usage: obj.testNoiseMultiplier(cls, num_microbatches) without return types