code: def testClippingNormWithMicrobatches(self, cls, num_microbatches):
      with self.cached_session() as sess:
          var0 = tf.Variable([0.0, 0.0])
          data0 = tf.Variable(
              [[3.0, 4.0], [6.0, 8.0], [-9.0, -12.0], [-12.0, -16.0]]
          )
  
          l2_norm_clip = 1.0
          dp_sum_query = gaussian_query.GaussianSumQuery(
              l2_norm_clip, 0.0
          )
  
          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
          var_np = self.evaluate(var0)
          self.assertAllClose([0.0, 0.0], var_np)
  
          # Compute expected gradient, which is the sum of differences.
          data_np = self.evaluate(data0)
          per_example_gradients = var_np - data_np
          mean_clipped_gradients = self._compute_expected_gradients(
              per_example_gradients, l2_norm_clip, num_microbatches
          )
  
          # Compare actual with expected gradients.
          gradient_op = opt.compute_gradients(
              self._loss(data0, var0), [var0]
          )
          grads_and_vars = sess.run(gradient_op)
          print("mean_clipped_gradients: ", mean_clipped_gradients)
          self.assertAllCloseAccordingToType(
              mean_clipped_gradients, grads_and_vars[0][0]
          )
  
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 6 for calling the DPOptimizerTest obj's underlying member method code with example usage: obj.testClippingNormWithMicrobatches(cls, num_microbatches) without return types