code: def et_optimizer(request):
      dimensions = request.param
      base_estimator = ExtraTreesRegressor(random_state=2)
      opt = Optimizer(
          dimensions,
          base_estimator,
          n_initial_points=1,
          acq_optimizer="sampling",
      )
      return opt
  
imports: from hyperparameter_hunter.optimization.backends.skopt.engine import Optimizer
  import numpy
  import pytest
  from scipy.optimize import OptimizeResult
  from sklearn.multioutput import MultiOutputRegressor
  from sklearn.utils.testing import assert_array_equal
  from skopt.benchmarks import bench1, bench1_with_time
  from skopt.learning import ExtraTreesRegressor, RandomForestRegressor
  from skopt.learning import GradientBoostingQuantileRegressor
label_desc: Use image node_id 1 for calling a global function with example usage: et_optimizer(request) and returns: opt