code: def test_sparse_inputs_with_features_in_secondary():
      rf = RandomForestClassifier(n_estimators=10, random_state=42)
      lr = LogisticRegression(multi_class="ovr", solver="liblinear")
      stclf = StackingCVClassifier(
          classifiers=[rf, rf],
          meta_classifier=lr,
          random_state=42,
          use_features_in_secondary=True,
      )
      X_train, _, y_train, _ = train_test_split(
          X_breast, y_breast, test_size=0.3
      )
  
      # dense
      stclf.fit(X_train, y_train)
  
      expected_value = 1.0
  
      assert (
          round(stclf.score(X_train, y_train), 2) == expected_value
      ), round(stclf.score(X_train, y_train), 2)
  
      # sparse
      stclf.fit(sparse.csr_matrix(X_train), y_train)
  
      if Version(sklearn_version) < Version("0.21"):
          expected_value = 1.0
      if Version(sklearn_version) < Version("0.22"):
          expected_value = 0.99
      else:
          expected_value = 1.00
  
      assert (
          round(stclf.score(X_train, y_train), 2) == expected_value
      ), round(stclf.score(X_train, y_train), 2)
  
imports: import random
  import numpy
  import pandas
  import pytest
  from packaging.version import Version
  from scipy import sparse
  from sklearn import __version__
  from sklearn import datasets, exceptions
  from sklearn.base import clone
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.linear_model import LogisticRegression, PassiveAggressiveClassifier
  from sklearn.metrics import roc_auc_score
  from sklearn.model_selection import GridSearchCV, KFold, cross_val_score, train_test_split
  from sklearn.naive_bayes import GaussianNB
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.svm import SVC
  from mlxtend.classifier import StackingCVClassifier
  from mlxtend.data import iris_data
  from mlxtend.externals.estimator_checks import NotFittedError
  from mlxtend.utils import assert_raises
label_desc: Use image node_id 23 for calling a global function with example usage: test_sparse_inputs_with_features_in_secondary() without return types