code: def test_compute_eps_tree(self, noise_multiplier, eps):
      orders = [1 + x / 10.0 for x in range(1, 100)] + list(
          range(12, 64)
      )
      # This tests is based on the StackOverflow setting in "Practical and
      # Private (Deep) Learning without Sampling or Shuffling". The calculated
      # epsilon could be better as the method in this package keeps improving.
      steps_list, target_delta = 1600, 1e-6
      rdp = tree_aggregation_accountant.compute_rdp_tree_restart(
          noise_multiplier, steps_list, orders
      )
      new_eps = dp_accounting.rdp.compute_epsilon(
          orders, rdp, target_delta
      )[0]
      self.assertLess(new_eps, eps)
  
file_path: privacy/tensorflow_privacy/privacy/analysis/tree_aggregation_accountant_test.py
imports: from absl.testing import parameterized
  import dp_accounting
  import tensorflow
  from tensorflow_privacy.privacy.analysis import tree_aggregation_accountant
label_desc: Use image node_id 1 for calling the TreeAggregationTest obj's underlying member method code with example usage: obj.test_compute_eps_tree(noise_multiplier, eps) without return types