code: def test_clfdrift_calibration(
      model,
      preds_type,
      p_val,
      n,
      n_features,
      binarize_preds,
      n_folds,
      train_size,
      preprocess_batch,
      update_x_ref,
      use_calibration,
      calibration_kwargs,
  ):
      """Testing calibration for various models and configurations."""
      np.random.seed(0)
  
      x_ref = np.random.randn(n, n_features)
      x_test0 = np.random.randn(n, n_features)
      x_test1 = np.random.randn(n, n_features) + 1
  
      to_list = False
      if preprocess_batch is not None:
          to_list = True
          x_ref = [_ for _ in x_ref]
          update_x_ref = None
  
      cd = ClassifierDriftSklearn(
          x_ref=x_ref,
          model=model,
          preds_type=preds_type,
          p_val=p_val,
          update_x_ref=update_x_ref,
          train_size=train_size,
          n_folds=n_folds,
          binarize_preds=binarize_preds,
          use_calibration=use_calibration,
          calibration_kwargs=calibration_kwargs,
      )
  
      if to_list:
          x_test0 = [_ for _ in x_test0]
      preds_0 = cd.predict(x_test0)
      assert cd.n == len(x_test0) + len(x_ref)
      assert preds_0["data"]["is_drift"] == 0
      assert preds_0["data"]["distance"] >= 0
  
      if to_list:
          x_test1 = [_ for _ in x_test1]
      preds_1 = cd.predict(x_test1)
      assert cd.n == len(x_test1) + len(x_test0) + len(x_ref)
      assert preds_1["data"]["is_drift"] == 1
      assert preds_1["data"]["distance"] >= 0
  
      assert preds_0["data"]["distance"] < preds_1["data"]["distance"]
      assert cd.meta["params"]["preds_type"] == preds_type
      assert cd.meta["params"]["binarize_preds "] == binarize_preds
  
imports: import pytest
  import numpy
  from typing import Union
  from alibi_detect.cd.sklearn.classifier import ClassifierDriftSklearn
  from sklearn.svm import SVC
  from sklearn.svm import LinearSVC
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.linear_model import LogisticRegression
  from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.gaussian_process import GaussianProcessClassifier
  from sklearn.neural_network import MLPClassifier
  from sklearn.naive_bayes import GaussianNB
  from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
label_desc: Use image node_id 2 for calling a global function with example usage: test_clfdrift_calibration(model, preds_type, p_val, n, n_features, binarize_preds, n_folds, train_size, preprocess_batch, update_x_ref, use_calibration, calibration_kwargs) without return types