code: def sample_gb_class(request):
      x = np.arange(100).reshape(5, 20)
      y = Surv.from_arrays(
          [False, False, True, True, False], [12, 14, 6, 9, 1]
      )
  
      return request.param, x, y
  
imports: from contextlib import nullcontext
  from os.path import dirname, join
  import numpy
  from numpy.testing import assert_array_almost_equal, assert_array_equal
  import pandas
  import pytest
  from sklearn.metrics import mean_squared_error
  from sksurv.column import categorical_to_numeric, standardize
  from sksurv.datasets import load_whas500
  from sksurv.ensemble import ComponentwiseGradientBoostingSurvivalAnalysis, GradientBoostingSurvivalAnalysis
  from sksurv.testing import FixtureParameterFactory, assert_cindex_almost_equal
  from sksurv.util import Surv
label_desc: Use image node_id 2 for calling a global function with example usage: sample_gb_class(request) and returns: request,  x,  y