docstr: Make sure normalization works with wrong model.
docstr_tok: ["Make","sure","normalization","works","with","wrong","model","."]
code: def test_normalization(self):
      """Make sure normalization works with wrong model."""
      np.random.seed(1)
  
      bounds = [
          {"name": "var_1", "type": "continuous", "domain": (1, 2)}
      ]
      # We get a first measurement at 1, but the model is so harshly
      # violated that we will just continue to get measurements at 1.
      # The minimum is at 2.
      x0 = np.array([[1]])
      f = lambda x: -1000 * x
  
      # Remove hyperparameter optimization, not needed here
      model = GPyOpt.models.GPModel(max_iters=0)
  
      opt = BayesianOptimization(
          f, bounds, X=x0, model=model, normalize_Y=True
      )
      opt.run_optimization(max_iter=1)
  
      # Make sure that we did not sample the same point again
      assert np.linalg.norm(opt.X[0] - opt.X[1]) > 1e-2
  
file_path: GPyOpt/GPyOpt/testing/methods_tests/test_bayesian_optimization.py
imports: import unittest
  import numpy
  import GPyOpt
  from GPyOpt.methods import BayesianOptimization
label_desc: Use image node_id 5 for calling the TestBayesianOptimization obj's underlying member method code with example usage: obj.test_normalization() without return types