class_docstr: Tests compute_score_lira.
class_docstr_tok: ["Tests","compute_score_lira","."]
code: def test_with_one_augmentation(
      self, option, fix_variance, median_or_mean, expected
  ):
      stat_target = np.array([[1.0], [0.0], [0.0], [0.0]])
      stat_in = [
          np.array([[-1], [1.0]]),
          np.array([[0], [2.0]]),
          np.array([[0], [20.0]]),
          np.empty((0, 1)),
      ]
      stat_out = [-s for s in stat_in]
  
      scores = amia.compute_score_lira(
          stat_target,
          stat_in,
          stat_out,
          option,
          fix_variance,
          median_or_mean,
      )
      np.testing.assert_allclose(scores, expected, atol=1e-7)
      # If `option` is "in" (resp. out), test with `stat_out` (resp. `stat_out`)
      # setting to empty list.
      if option == "in":
          scores = amia.compute_score_lira(
              stat_target,
              stat_in,
              [],
              option,
              fix_variance,
              median_or_mean,
          )
          np.testing.assert_allclose(scores, expected, atol=1e-7)
      elif option == "out":
          scores = amia.compute_score_lira(
              stat_target,
              [],
              stat_out,
              option,
              fix_variance,
              median_or_mean,
          )
          np.testing.assert_allclose(scores, expected, atol=1e-7)
  
imports: from absl.testing import absltest
  from absl.testing import parameterized
  import numpy
  from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack import advanced_mia
label_desc: Use image node_id 1 for calling the TestLiRA obj's underlying member method code with example usage: obj.test_with_one_augmentation(option, fix_variance, median_or_mean, expected) without return types