for hists, indices in (
                (hists_parent, sample_indices),
                (hists_left, sample_indices_left),
                (hists_right, sample_indices_right)):
			...
            gradients = hists['sum_gradients'].sum(axis=1)  # shape = (n_features,)
            expected_gradient = all_gradients[indices].sum()  # scalar
			...
>           assert np.allclose(gradients, expected_gradient)
E           assert False
E            +  where False = <function allclose at 0xf7209adc>(array([  2.68156159e+154,   8.09900937e+000,  -5.20938628e+238,\n         2.01738272e+118,  -2.20855883e+071,   4.74963...287e+000,\n         9.24455781e+000,   6.38250258e-001,  -3.41402339e+243,\n        -9.90611303e-001,   6.27710174e+057]), 26.061348)
E            +    where <function allclose at 0xf7209adc> = np.allclose