code: def test_nan_and_string_input(X):
      """Test X containing nan and string with check_input=False."""
      y = np.array([1, 0, 1, 0, 1])
      clf = Pipeline(
          [
              ("encoder", OrdinalEncoder()),
              ("gbm", HistGradientBoostingClassifier()),
          ]
      )
      transformer = EstimatorTransformer(clf, check_input=False)
      transformed = transformer.fit(X, y).transform(X)
  
      assert transformed.shape == (y.shape[0], 1)
      assert np.all(transformed == clf.fit(X, y).predict(X))
  
imports: from unittest.mock import Mock, patch
  import numpy
  import pytest
  from sklearn import clone
  from sklearn.dummy import DummyClassifier
  from sklearn.ensemble import HistGradientBoostingClassifier
  from sklearn.linear_model import LinearRegression, Ridge
  from sklearn.multioutput import MultiOutputRegressor
  from sklearn.pipeline import FeatureUnion, Pipeline
  from sklearn.preprocessing import OrdinalEncoder
  from sklearn.utils import check_X_y
  from sklego.common import flatten
  from sklego.meta import EstimatorTransformer
  from tests.conftest import general_checks, transformer_checks
label_desc: Use image node_id 8 for calling a global function with example usage: test_nan_and_string_input(X) without return types