code: def test_trimming_bounds():
      """Test if the bounds are trimmed correctly within the bounds"""
  
      def dummy_function(x1, x2, x3, x4, x5):
          return 0.0
  
      min_window = 1.0
      bounds_transformer = SequentialDomainReductionTransformer(
          minimum_window=min_window
      )
      pbounds = {
          "x1": (-1, 0.6),
          "x2": (-1, 0.5),
          "x3": (-0.4, 0.6),
          "x4": (0.3, 1.3),
          "x5": (-1, 0.8),
      }
      target_sp = TargetSpace(
          target_func=dummy_function, pbounds=pbounds
      )
      bounds_transformer.initialize(target_sp)
      new_bounds = np.concatenate(
          (np.ones((5, 1)) * 0.1, np.ones((5, 1))), axis=1
      )
      global_bounds = np.asarray(list(pbounds.values()))
  
      trimmed_bounds = bounds_transformer._trim(
          new_bounds, global_bounds
      )
      # check that the bounds are trimmed to the minimum window
      # raises ValueError if the bounds are not trimmed correctly
      bounds_transformer._window_bounds_compatibility(trimmed_bounds)
  
file_path: BayesianOptimization/tests/test_seq_domain_red.py
imports: import numpy
  import pytest
  from bayes_opt import SequentialDomainReductionTransformer
  from bayes_opt import BayesianOptimization
  from bayes_opt.target_space import TargetSpace
label_desc: Use image node_id 5 for calling a global function with example usage: test_trimming_bounds() and returns: int