code: def test_feature_names_in():
      data = load_iris()
      x = pd.DataFrame(data["data"], columns=data["feature_names"])
      y = data["target"]
  
      meta = Stacking(
          LogisticRegression(),
          [
              (
                  "tree",
                  DecisionTreeClassifier(max_depth=1, random_state=0),
              ),
              (
                  "svm",
                  SVC(probability=True, gamma="auto", random_state=0),
              ),
          ],
      )
      meta.fit(x, y)
      assert meta.n_features_in_ == len(data["feature_names"])
      assert_array_equal(meta.feature_names_in_, data["feature_names"])
  
      meta.fit(x.values, y)
      assert meta.n_features_in_ == len(data["feature_names"])
      with pytest.raises(
          AttributeError,
          match="'Stacking' object has no attribute 'feature_names_in_'",
      ):
          meta.feature_names_in_
  
imports: import numpy
  from numpy.testing import assert_array_almost_equal, assert_array_equal
  import pandas
  import pytest
  from sklearn.base import BaseEstimator
  from sklearn.datasets import load_iris
  from sklearn.linear_model import LogisticRegression
  from sklearn.metrics import accuracy_score, roc_auc_score
  from sklearn.svm import SVC
  from sklearn.tree import DecisionTreeClassifier
  from sksurv.ensemble import RandomSurvivalForest
  from sksurv.linear_model import CoxPHSurvivalAnalysis
  from sksurv.meta import MeanEstimator, Stacking
  from sksurv.svm import FastSurvivalSVM
  from sksurv.testing import assert_chf_properties, assert_cindex_almost_equal, assert_survival_function_properties
label_desc: Use image node_id 9 for calling the TestStackingClassifier obj's underlying member method code with example usage: obj.test_feature_names_in() without return types