code: def test_ebm_missing():
      data = synthetic_regression()
      X = data["full"]["X"]
      y = data["full"]["y"]
  
      X[0, 0] = np.nan
  
      with warnings.catch_warnings():
          warnings.filterwarnings("ignore", "Missing values detected.*")
  
          clf = ExplainableBoostingRegressor(n_jobs=-2, interactions=0)
          clf.fit(X, y)
          clf.predict(X)
  
          valid_ebm(clf)
  
imports: import math
  from sklearn.metrics import log_loss
  from interpret.utils import make_synthetic
  from ...tutils import synthetic_multiclass, synthetic_classification, iris_classification, smoke_test_explanations
  from ...tutils import synthetic_regression
  from interpret.glassbox import ExplainableBoostingRegressor, ExplainableBoostingClassifier
  from interpret.privacy import DPExplainableBoostingClassifier, DPExplainableBoostingRegressor
  from interpret.utils import inv_link
  from io import StringIO
  import json
  import numpy
  import pandas
  from sklearn.model_selection import train_test_split
  from sklearn.metrics import accuracy_score
  from sklearn.utils.estimator_checks import check_estimator
  import pytest
  import warnings
label_desc: Use image node_id 19 for calling a global function with example usage: test_ebm_missing() without return types