code: def data_simple(self):
      y = Surv.from_arrays(
          [True, False, False, True, False],
          [7.0, 8.0, 11.0, 11.0, 23.0],
          name_event="D",
          name_time="Y",
      )
  
      x = pd.DataFrame(
          {
              "F1": [1, 1, 1, 0, 0],
              "F2": [23, 43, 54, 75, 67],
              "F3": [120, 98, 78, 91, 79],
              "F4": [0.123, 0.541, 0.784, 0.846, 0.331],
          }
      )
  
      kwargs = {"alpha_min_ratio": 0.0001, "l1_ratio": 1.0}
  
      expected_alphas = np.array(
          [
              7.02666666666667,
              6.40243696630484,
              5.83366211207401,
              5.31541564828386,
              4.84320877198972,
              4.41295145312887,
              4.02091700863675,
              3.66370982370111,
              3.3382359405709,
              3.04167626017436,
              2.77146212443153,
              2.52525306776672,
              2.30091654511542,
              2.09650946083909,
              1.91026133856035,
              1.74055898614351,
              1.5859325229961,
              1.44504264866632,
              1.31666904246323,
              1.19969979362274,
              1.09312177046848,
              0.996011845149902,
              0.907528897950459,
              0.826906531910992,
              0.753446434665921,
              0.686512329995589,
              0.625524466706047,
              0.569954597101554,
              0.519321401555745,
              0.473186319551291,
              0.431149751078499,
              0.392847595491192,
              0.357948097841098,
              0.326148975375191,
              0.297174799307102,
              0.270774609184727,
              0.24671973919085,
              0.22480183754923,
              0.204831061881182,
              0.186634434881721,
              0.170054346072885,
              0.154947186657187,
              0.141182105646904,
              0.128639876495421,
              0.117211864413924,
              0.106799085428826,
              0.0973113490299429,
              0.0886664769834391,
              0.0807895915432809,
              0.0736124668960205,
              0.0670729382214382,
          ]
      )
  
      expected_coef = pd.read_csv(
          SIMPLE_COEF_FILE, header=None, skiprows=1
      )
  
      expected = CoxnetExpectation(
          alphas=expected_alphas,
          coef=expected_coef,
      )
  
      return kwargs, x, y, expected
  
imports: from dataclasses import dataclass
  from os.path import dirname, join
  from typing import Optional
  import numpy
  from numpy.testing import assert_array_almost_equal
  import pandas
  import pytest
  from sklearn.exceptions import ConvergenceWarning
  from sklearn.pipeline import make_pipeline
  from sksurv.datasets import get_x_y, load_breast_cancer
  from sksurv.linear_model.coxnet import CoxnetSurvivalAnalysis
  from sksurv.preprocessing import OneHotEncoder
  from sksurv.testing import FixtureParameterFactory
  from sksurv.util import Surv
label_desc: Use image node_id 12 for calling the CoxnetCases obj's underlying member method code with example usage: obj.data_simple() and returns: kwargs,  x,  y,  expected