code: def test_gbrt_base_estimator():
      rng = np.random.RandomState(1)
      N = 10000
      X = np.ones((N, 1))
      y = rng.normal(size=N)
  
      base = RandomForestRegressor()
      rgr = GradientBoostingQuantileRegressor(base_estimator=base)
      with pytest.raises(ValueError):
          # 'type GradientBoostingRegressor',
          rgr.fit(X, y)
  
      base = GradientBoostingRegressor()
      rgr = GradientBoostingQuantileRegressor(base_estimator=base)
      with pytest.raises(ValueError):
          # 'quantile loss'
          rgr.fit(X, y)
  
      base = GradientBoostingRegressor(loss="quantile", n_estimators=20)
      rgr = GradientBoostingQuantileRegressor(base_estimator=base)
      rgr.fit(X, y)
  
      estimates = rgr.predict(X, return_quantiles=True)
      assert_almost_equal(
          stats.norm.ppf(rgr.quantiles),
          np.mean(estimates, axis=0),
          decimal=2,
      )
  
file_path: scikit-optimize/skopt/learning/tests/test_gbrt.py
imports: import numpy
  import pytest
  from scipy import stats
  from sklearn.ensemble import GradientBoostingRegressor
  from sklearn.ensemble import RandomForestRegressor
  from numpy.testing import assert_equal
  from numpy.testing import assert_array_equal
  from numpy.testing import assert_almost_equal
  from skopt.learning import GradientBoostingQuantileRegressor
label_desc: Use image node_id 3 for calling a global function with example usage: test_gbrt_base_estimator() without return types