code: def opt_svc_0(request):
      optimizer = BayesianOptPro(
          target_metric=request.param, iterations=2, random_state=32
      )
      optimizer.forge_experiment(
          model_initializer=SVC,
          model_init_params=dict(
              C=Real(0.9, 1.1),
              kernel=Categorical(["linear", "poly", "rbf"]),
              max_iter=Integer(50, 125),
              tol=1e-3,
          ),
      )
      optimizer.go()
      yield optimizer
  
      assert optimizer.target_metric == (
          "oof",
          (request.param or "roc_auc"),
      )
  
imports: from hyperparameter_hunter import Environment, CVExperiment, Real, Integer, Categorical
  from hyperparameter_hunter import BayesianOptPro, ExtraTreesOptPro, lambda_callback
  from hyperparameter_hunter.callbacks.recipes import confusion_matrix_oof, confusion_matrix_holdout
  from hyperparameter_hunter.i_o.recorders import YAMLDescriptionRecorder, UnsortedIDLeaderboardRecorder
  from hyperparameter_hunter.i_o.result_reader import has_experiment_result_file
  from hyperparameter_hunter.utils.learning_utils import get_toy_classification_data, get_breast_cancer_data
  import pytest
  from sklearn.ensemble import GradientBoostingClassifier
  from sklearn.metrics import f1_score
  from sklearn.model_selection import RepeatedStratifiedKFold, StratifiedKFold
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.svm import SVC
  from sklearn.tree import DecisionTreeClassifier
label_desc: Use image node_id 11 for calling a global function with example usage: opt_svc_0(request) without return types