code: def test_returns_result_object():
      base_estimator = ExtraTreesRegressor(random_state=2)
      opt = Optimizer(
          [(-2.0, 2.0)],
          base_estimator,
          n_initial_points=1,
          acq_optimizer="sampling",
      )
      result = opt.tell([1.5], 2.0)
  
      assert isinstance(result, OptimizeResult)
      assert_equal(len(result.x_iters), len(result.func_vals))
      assert_equal(np.min(result.func_vals), result.fun)
  
imports: import numpy
  import pytest
  from sklearn.multioutput import MultiOutputRegressor
  from numpy.testing import assert_array_equal
  from numpy.testing import assert_equal
  from numpy.testing import assert_raises
  from skopt import gp_minimize
  from skopt import forest_minimize
  from skopt.benchmarks import bench1, bench1_with_time
  from skopt.benchmarks import branin
  from skopt.learning import ExtraTreesRegressor, RandomForestRegressor
  from skopt.learning import GradientBoostingQuantileRegressor
  from skopt.optimizer import Optimizer
  from scipy.optimize import OptimizeResult
label_desc: Use image node_id 11 for calling a global function with example usage: test_returns_result_object() without return types