code: def test_predict(rossi):
      cph = CoxPHSurvivalAnalysis()
      xc = standardize(rossi.x, with_std=False)
      cph.fit(xc.values, rossi.y)
  
      expected = np.array(
          [
              -0.136002823953217,
              -1.13104636905577,
              0.741965816026403,
              -0.98072115186145,
              -0.600098931134794,
              -0.997407014712788,
              -0.0993800739865776,
              -0.266761246895696,
              -0.665145743277517,
              -0.418747210463951,
              -0.0770761787926419,
              0.411385264707043,
              -0.0770761787926419,
              0.563114305747799,
              -1.07096133044073,
          ]
      )
  
      idx = np.array(
          [
              15,
              77,
              79,
              90,
              113,
              122,
              134,
              172,
              213,
              219,
              257,
              313,
              364,
              395,
              409,
          ]
      )
  
      pred = cph.predict(xc.iloc[idx, :].values)
  
      assert_array_almost_equal(expected, pred)
  
imports: import warnings
  import numpy
  from numpy.testing import assert_array_almost_equal
  import pandas
  import pytest
  from scipy.optimize import check_grad
  from sklearn.exceptions import ConvergenceWarning
  from sklearn.pipeline import make_pipeline
  from sklearn.preprocessing import StandardScaler
  from sksurv.column import standardize
  from sksurv.datasets import load_breast_cancer
  from sksurv.linear_model.coxph import CoxPHOptimizer, CoxPHSurvivalAnalysis
  from sksurv.preprocessing import OneHotEncoder
label_desc: Use image node_id 7 for calling the TestCoxPH obj's underlying member method code with example usage: obj.test_predict(rossi) without return types