code: def testBaseline(self, cls, num_microbatches, expected_answer):
      with self.cached_session() as sess:
          var0 = tf.Variable([1.0, 2.0])
          data0 = tf.Variable(
              [[3.0, 4.0], [5.0, 6.0], [7.0, 8.0], [-1.0, 0.0]]
          )
  
          opt = cls(
              l2_norm_clip=1.0e9,
              noise_multiplier=0.0,
              num_microbatches=num_microbatches,
              learning_rate=2.0,
          )
  
          self.evaluate(tf.compat.v1.global_variables_initializer())
          # Fetch params to validate initial values
          self.assertAllClose([1.0, 2.0], self.evaluate(var0))
  
          # Expected gradient is sum of differences divided by number of
          # microbatches.
          gradient_op = opt.compute_gradients(
              self._loss(data0, var0), [var0]
          )
          grads_and_vars = sess.run(gradient_op)
          self.assertAllCloseAccordingToType(
              expected_answer, 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 3 for calling the DPOptimizerTest obj's underlying member method code with example usage: obj.testBaseline(cls, num_microbatches, expected_answer) without return types