code: def test_whas500(make_whas500, whas500_true_x):
      whas500 = make_whas500(with_mean=False, with_std=False)
      time = whas500.y["lenfol"]
      event = whas500.y["fstat"]
  
      x, y = kaplan_meier_estimator(event, time)
  
      assert_array_equal(x.astype(int), whas500_true_x)
  
      true_y = np.array(
          [
              0.984,
              0.968,
              0.962,
              0.958,
              0.954,
              0.944,
              0.932,
              0.926,
              0.918,
              0.914,
              0.912,
              0.908,
              0.902,
              0.896,
              0.892,
              0.888,
              0.886,
              0.884,
              0.88,
              0.874,
              0.872,
              0.87,
              0.868,
              0.866,
              0.864,
              0.862,
              0.86,
              0.858,
              0.854,
              0.852,
              0.85,
              0.848,
              0.844,
              0.84,
              0.838,
              0.836,
              0.834,
              0.832,
              0.83,
              0.828,
              0.826,
              0.824,
              0.822,
              0.82,
              0.818,
              0.816,
              0.814,
              0.812,
              0.81,
              0.808,
              0.806,
              0.804,
              0.802,
              0.8,
              0.798,
              0.794,
              0.792,
              0.79,
              0.788,
              0.786,
              0.784,
              0.78,
              0.776,
              0.774,
              0.772,
              0.77,
              0.768,
              0.766,
              0.764,
              0.76,
              0.758,
              0.756,
              0.754,
              0.752,
              0.75,
              0.746,
              0.744,
              0.742,
              0.74,
              0.738,
              0.736,
              0.734,
              0.732,
              0.73,
              0.726,
              0.724,
              0.724,
              0.724,
              0.724,
              0.724,
              0.721960563380282,
              0.719921126760564,
              0.719921126760564,
              0.719921126760564,
              0.717864209255533,
              0.715807291750503,
              0.715807291750503,
              0.715807291750503,
              0.715807291750503,
              0.715807291750503,
              0.713714287973455,
              0.711621284196407,
              0.711621284196407,
              0.711621284196407,
              0.711621284196407,
              0.711621284196407,
              0.711621284196407,
              0.711621284196407,
              0.709484283342964,
              0.709484283342964,
              0.70734082629359,
              0.70734082629359,
              0.70734082629359,
              0.70734082629359,
              0.70734082629359,
              0.70734082629359,
              0.70734082629359,
              0.705150916614662,
              0.705150916614662,
              0.702926465773606,
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              0.700621788836644,
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              0.69830950570517,
              0.695997222573696,
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              0.693669472665422,
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              0.691325994717228,
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              0.691325994717228,
              0.688865759860583,
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              0.686387825472596,
              0.683900913061463,
              0.68141400065033,
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              0.68141400065033,
              0.678871411095665,
              0.678871411095665,
              0.676319262933651,
              0.673767114771638,
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              0.671114488335529,
              0.66846186189942,
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              0.665788014451822,
              0.663114167004225,
              0.660440319556627,
              0.657766472109029,
              0.655092624661431,
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              0.652396770238956,
              0.649700915816481,
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              0.646993828667246,
              0.644286741518011,
              0.641579654368776,
              0.641579654368776,
              0.638861096511281,
              0.636142538653786,
              0.633423980796291,
              0.630705422938796,
              0.627986865081301,
              0.625268307223807,
              0.622549749366312,
              0.619831191508817,
              0.617112633651322,
              0.614394075793827,
              0.611675517936333,
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              0.608734770253946,
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              0.605721231787343,
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              0.602692625628406,
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              0.599601894214927,
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              0.596478967682557,
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              0.596478967682557,
              0.593183503772709,
              0.593183503772709,
              0.593183503772709,
              0.593183503772709,
              0.589832184542355,
              0.589832184542355,
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              0.589832184542355,
              0.586422749949625,
              0.582973204361686,
              0.582973204361686,
              0.582973204361686,
              0.582973204361686,
              0.582973204361686,
              0.582973204361686,
              0.582973204361686,
              0.582973204361686,
              0.582973204361686,
              0.582973204361686,
              0.582973204361686,
              0.582973204361686,
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              0.582973204361686,
              0.582973204361686,
              0.582973204361686,
              0.582973204361686,
              0.579112454663926,
              0.579112454663926,
              0.579112454663926,
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              0.579112454663926,
              0.579112454663926,
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              0.579112454663926,
              0.575034197940941,
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              0.575034197940941,
              0.570506369610697,
              0.570506369610697,
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              0.570506369610697,
              0.561153806174456,
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              0.292056629812476,
              0.146028314906238,
              0,
          ]
      )
  
      assert_array_almost_equal(y, true_y)
  
imports: from os.path import dirname, join
  import numpy
  from numpy.testing import assert_array_almost_equal, assert_array_equal
  import pandas
  import pytest
  from sksurv.nonparametric import CensoringDistributionEstimator, SurvivalFunctionEstimator, kaplan_meier_estimator, nelson_aalen_estimator
  from sksurv.testing import FixtureParameterFactory
  from sksurv.util import Surv
label_desc: Use image node_id 9 for calling the TestKaplanMeier obj's underlying member method code with example usage: obj.test_whas500(make_whas500, whas500_true_x) without return types