code: def test_fit(self, whas500_sparse_data, loss):
      model = self.assert_fit_and_predict(
          whas500_sparse_data,
          loss=loss,
          n_estimators=100,
          max_depth=1,
          min_samples_split=10,
          subsample=0.5,
      )
      assert model.oob_improvement_.shape == (100,)
  
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 the TestSparseGradientBoosting obj's underlying member method code with example usage: obj.test_fit(whas500_sparse_data, loss) without return types