code: def testBaseline(self, cls, num_microbatches, expected_answer):
      with tf.GradientTape(persistent=True) as gradient_tape:
          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]]
          )
  
          dp_sum_query = gaussian_query.GaussianSumQuery(1.0e9, 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
          self.assertAllClose([1.0, 2.0], self.evaluate(var0))
  
          # Expected gradient is sum of differences divided by number of
          # microbatches.
          grads_and_vars = opt.compute_gradients(
              lambda: self._loss_fn(var0, data0),
              [var0],
              gradient_tape=gradient_tape,
          )
          self.assertAllCloseAccordingToType(
              expected_answer, 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 3 for calling the DPOptimizerEagerTest obj's underlying member method code with example usage: obj.testBaseline(cls, num_microbatches, expected_answer) without return types