code: def _fit_svc(n_jobs=1, n_points=1, cv=None):
      """
      Utility function to fit a larger classification task with SVC
      """
  
      X, y = make_classification(
          n_samples=1000,
          n_features=20,
          n_redundant=0,
          n_informative=18,
          random_state=1,
          n_clusters_per_class=1,
      )
  
      opt = BayesSearchCV(
          SVC(),
          {
              "C": Real(1e-3, 1e3, prior="log-uniform"),
              "gamma": Real(1e-3, 1e1, prior="log-uniform"),
              "degree": Integer(1, 3),
          },
          n_jobs=n_jobs,
          n_iter=11,
          n_points=n_points,
          cv=cv,
          random_state=42,
      )
  
      opt.fit(X, y)
      assert opt.score(X, y) > 0.9
  
      opt2 = BayesSearchCV(
          SVC(),
          {
              "C": Real(1e-3, 1e3, prior="log-uniform"),
              "gamma": Real(1e-3, 1e1, prior="log-uniform"),
              "degree": Integer(1, 3),
          },
          n_jobs=n_jobs,
          n_iter=11,
          n_points=n_points,
          cv=cv,
          random_state=42,
      )
  
      opt2.fit(X, y)
  
      assert opt.score(X, y) == opt2.score(X, y)
  
imports: import pytest
  from sklearn.datasets import load_iris, make_classification
  from sklearn.model_selection import train_test_split
  from sklearn.pipeline import Pipeline
  from sklearn.svm import SVC, LinearSVC
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.base import clone
  from sklearn.base import BaseEstimator
  from scipy.stats import rankdata
  import numpy
  from numpy.testing import assert_array_equal
  from skopt.space import Real, Categorical, Integer
  from skopt import BayesSearchCV
label_desc: Use image node_id 1 for calling a global function with example usage: _fit_svc(n_jobs, n_points, cv) without return types