code: def test_fit_and_predict(make_whas500):
      whas500 = make_whas500(
          with_mean=True, with_std=True, to_numeric=True
      )
      meta = _create_regression_ensemble()
      assert len(meta) == 0
  
      meta.fit(whas500.x_data_frame.iloc[:400], whas500.y[:400])
      assert meta.scores_.shape[0] >= len(meta)
      assert len(meta) == 5
      assert meta.scores_.shape[0] == 9
  
      feature_names = whas500.x_data_frame.columns.values
      assert meta.n_features_in_ == len(feature_names)
      assert_array_equal(meta.feature_names_in_, feature_names)
  
      p = meta.predict(whas500.x_data_frame.iloc[400:])
      score = np.sqrt(mean_squared_error(whas500.y[400:]["lenfol"], p))
      assert abs(score - 423.82894756865056) <= 0.1
  
      c_index = meta.score(
          whas500.x_data_frame.iloc[:400], whas500.y[:400]
      )
      assert c_index == pytest.approx(0.767067946974157)
  
imports: import numpy
  from numpy.testing import assert_array_equal
  import pytest
  from sklearn.dummy import DummyRegressor
  from sklearn.metrics import mean_squared_error
  from sklearn.model_selection import KFold, ParameterGrid
  from sksurv.ensemble import ComponentwiseGradientBoostingSurvivalAnalysis
  from sksurv.kernels import ClinicalKernelTransform
  from sksurv.linear_model import IPCRidge
  from sksurv.meta import EnsembleSelection, EnsembleSelectionRegressor
  from sksurv.metrics import concordance_index_censored
  from sksurv.svm import FastKernelSurvivalSVM, FastSurvivalSVM
  from sksurv.testing import FixtureParameterFactory, assert_cindex_almost_equal
  from sksurv.tree import SurvivalTree
  from sksurv.util import check_array_survival
label_desc: Use image node_id 1 for calling the TestEnsembleSelectionRegressor obj's underlying member method code with example usage: obj.test_fit_and_predict(make_whas500) without return types