code: def test_ebm_only_missing():
      X = np.full((10, 10), np.nan)
      y = np.full(10, 0)
      y[0] = 1
  
      with warnings.catch_warnings():
          warnings.filterwarnings("ignore", "Missing values detected.*")
  
          clf = ExplainableBoostingClassifier(n_jobs=1)
          clf.fit(X, y)
          clf.predict(X)
          clf.explain_global()
          clf.explain_local(X, y)
  
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 20 for calling a global function with example usage: test_ebm_only_missing() without return types