code: def test_sparse_inputs():
      np.random.seed(123)
      rf = RandomForestClassifier(n_estimators=10)
      lr = LogisticRegression(multi_class="ovr", solver="liblinear")
      stclf = StackingCVClassifier(
          classifiers=[rf, rf], meta_classifier=lr, random_state=42
      )
      X_train, X_test, y_train, y_test = train_test_split(
          X_breast, y_breast, test_size=0.3
      )
  
      # dense
      stclf.fit(X_train, y_train)
      assert round(stclf.score(X_train, y_train), 2) == 0.99
  
      # sparse
      stclf.fit(sparse.csr_matrix(X_train), y_train)
      assert round(stclf.score(X_train, y_train), 2) == 0.99
  
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 22 for calling a global function with example usage: test_sparse_inputs() without return types