code: def testClippingNorm(self, cls):
      with self.cached_session() as sess:
          var0 = tf.Variable([0.0, 0.0])
          data0 = tf.Variable([[3.0, 4.0], [6.0, 8.0]])
  
          dp_sum_query = gaussian_query.GaussianSumQuery(1.0, 0.0)
  
          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, 0.0], self.evaluate(var0))
  
          # Expected gradient is sum of differences.
          gradient_op = opt.compute_gradients(
              self._loss(data0, var0), [var0]
          )
          grads_and_vars = sess.run(gradient_op)
          self.assertAllCloseAccordingToType(
              [-0.6, -0.8], 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 5 for calling the DPOptimizerTest obj's underlying member method code with example usage: obj.testClippingNorm(cls) without return types