code: def test_dp_sgd_privacy_statement_no_user_dp_with_rdp(self):
      statement = (
          compute_dp_sgd_privacy_lib.compute_dp_sgd_privacy_statement(
              **DP_SGD_STATEMENT_KWARGS,
              accountant_type=_RDP,
          )
      )
      expected_statement = """\
  DP-SGD performed over 10000 examples with 64 examples per iteration, noise
  multiplier 2.0 for 5.0 epochs with microbatching, and no bound on number of
  examples per user.
  
  This privacy guarantee protects the release of all model checkpoints in addition
  to the final model.
  
  Example-level DP with add-or-remove-one adjacency at delta = 1e-06 computed with
  RDP accounting:
      Epsilon with each example occurring once per epoch:        13.376
      Epsilon assuming Poisson sampling (*):                      1.616
  
  No user-level privacy guarantee is possible without a bound on the number of
  examples per user.
  
  (*) Poisson sampling is not usually done in training pipelines, but assuming
  that the data was randomly shuffled, it is believed that the actual epsilon
  should be closer to this value than the conservative assumption of an arbitrary
  data order.
  """
      self.assertEqual(statement, expected_statement)
  
file_path: privacy/tensorflow_privacy/privacy/analysis/compute_dp_sgd_privacy_test.py
imports: import math
  from absl.testing import absltest
  from absl.testing import parameterized
  from tensorflow_privacy.privacy.analysis import compute_dp_sgd_privacy_lib
label_desc: Use image node_id 7 for calling the ComputeDpSgdPrivacyTest obj's underlying member method code with example usage: obj.test_dp_sgd_privacy_statement_no_user_dp_with_rdp() without return types