code: def testClippingNorm(self, cls):
      with tf.GradientTape(persistent=True) as gradient_tape:
          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.
          grads_and_vars = opt.compute_gradients(
              lambda: self._loss_fn(var0, data0),
              [var0],
              gradient_tape=gradient_tape,
          )
          self.assertAllCloseAccordingToType(
              [-0.6, -0.8], grads_and_vars[0][0]
          )
  
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 4 for calling the DPOptimizerEagerTest obj's underlying member method code with example usage: obj.testClippingNorm(cls) without return types