code: def data_example_2_normalize(self):
      x, y = self.get_example()
  
      kwargs = {
          "alpha_min_ratio": 0.0001,
          "l1_ratio": 0.9,
          "normalize": True,
      }
  
      expected_alphas = np.array(
          [
              0.00831887750878913,
              0.00757985135869546,
              0.0069064782549347,
              0.00629292576181799,
              0.00573387958116825,
              0.00522449752241159,
              0.00476036756183909,
              0.00433746962776807,
              0.00395214078060438,
              0.00360104348621191,
              0.00328113670778857,
              0.00298964956586034,
              0.00272405733824878,
              0.00248205959213538,
              0.00226156025881296,
              0.00206064947854128,
              0.00187758705825562,
              0.00171078739884696,
              0.00155880576146105,
              0.00142032575386155,
              0.0012941479284703,
              0.00117917939332615,
              0.00107442434597765,
              0.000978975448318574,
              0.000892005967659349,
              0.000812762615963956,
              0.000740559025229353,
              0.000674769802494178,
              0.000614825112984099,
              0.000560205744475609,
              0.000510438610128125,
              0.000465092650832102,
              0.000423775101581237,
              0.000386128089529882,
              0.000351825534269655,
              0.000320570323476944,
              0.000292091739468086,
              0.000266143114372309,
              0.000242499693612633,
              0.000220956689189248,
              0.000201327505903829,
              0.00018344212516119,
              0.000167145632349552,
              0.000152296875044284,
              0.000138767240413126,
              0.000126439541233365,
              0.000115207000872171,
              0.000104972328438487,
              9.5646876095872e-05,
              8.71498712373515e-05,
              7.94077168717352e-05,
              7.23533541616886e-05,
              6.59256815921643e-05,
              6.00690257383086e-05,
              5.47326590488896e-05,
          ]
      )
  
      expected_coef = self.get_example_coef("2-norm")
  
      expected_pred = np.array(
          [
              0,
              0.000639408747219786,
              -0.00052949581811348,
              -0.00655544513119031,
              -0.0126379123842854,
              -0.0154908462985011,
              -0.0173929193691804,
              -0.019286506654261,
              -0.0211630424740022,
              -0.0298515402290131,
              -0.0415450906301397,
              -0.0561550947027854,
              -0.0794886384588791,
              -0.102298331006247,
              -0.124312860420833,
              -0.145023690076741,
              -0.161527852664137,
              -0.177495378416267,
              -0.192896322287149,
              -0.207713020484007,
              -0.221931814547868,
              -0.235542897596703,
              -0.248302871817807,
              -0.258066101598933,
              -0.267578020734177,
              -0.276668591810926,
              -0.285326398847612,
              -0.293735142605935,
              -0.301713974346824,
              -0.308695984478683,
              -0.314247838968584,
              -0.319176436422011,
              -0.325263490815563,
              -0.331441729472062,
              -0.338538969805117,
              -0.344920772188639,
              -0.351102580604316,
              -0.356876599562591,
              -0.363158677531647,
              -0.369352370626939,
              -0.375075124027788,
              -0.380357592110288,
              -0.385230301184507,
              -0.3897221161163,
              -0.393859049671187,
              -0.395986139303465,
              -0.397770798375561,
              -0.399167571117783,
              -0.400486682367687,
              -0.40143075878309,
              -0.402309388629067,
              -0.403119489802433,
              -0.403943664491923,
              -0.404691055430968,
              -0.405377242302289,
          ]
      )
  
      expected = CoxnetExpectation(
          alphas=expected_alphas,
          coef=expected_coef,
          pred=expected_pred,
      )
  
      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 8 for calling the CoxnetCases obj's underlying member method code with example usage: obj.data_example_2_normalize() and returns: kwargs,  x,  y,  expected