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]])
  
          opt = cls(
              l2_norm_clip=1.0,
              noise_multiplier=0.0,
              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 unittest
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from tensorflow import estimator
  from tensorflow.compat.v1 import estimator
  from tensorflow_privacy.privacy.optimizers import dp_optimizer_vectorized
  from tensorflow_privacy.privacy.optimizers.dp_optimizer_vectorized import VectorizedDPAdagrad
  from tensorflow_privacy.privacy.optimizers.dp_optimizer_vectorized import VectorizedDPAdam
  from tensorflow_privacy.privacy.optimizers.dp_optimizer_vectorized import VectorizedDPSGD
label_desc: Use image node_id 4 for calling the DPOptimizerTest obj's underlying member method code with example usage: obj.testClippingNorm(cls) without return types