code: def test_classifier_chain_fit_and_predict_with_sparse_data(
      csr_container,
  ):
      # Fit classifier chain with sparse data
      X, Y = generate_multilabel_dataset_with_correlations()
      X_sparse = csr_container(X)
  
      classifier_chain = ClassifierChain(LogisticRegression())
      classifier_chain.fit(X_sparse, Y)
      Y_pred_sparse = classifier_chain.predict(X_sparse)
  
      classifier_chain = ClassifierChain(LogisticRegression())
      classifier_chain.fit(X, Y)
      Y_pred_dense = classifier_chain.predict(X)
  
      assert_array_equal(Y_pred_sparse, Y_pred_dense)
  
imports: import re
  import numpy
  import pytest
  from joblib import cpu_count
  from sklearn import datasets
  from sklearn.base import ClassifierMixin, clone
  from sklearn.datasets import load_linnerud, make_classification, make_multilabel_classification, make_regression
  from sklearn.dummy import DummyClassifier, DummyRegressor
  from sklearn.ensemble import GradientBoostingRegressor, RandomForestClassifier, StackingRegressor
  from sklearn.exceptions import NotFittedError
  from sklearn.impute import SimpleImputer
  from sklearn.linear_model import Lasso, LinearRegression, LogisticRegression, OrthogonalMatchingPursuit, PassiveAggressiveClassifier, Ridge, SGDClassifier, SGDRegressor
  from sklearn.metrics import jaccard_score, mean_squared_error
  from sklearn.model_selection import GridSearchCV, train_test_split
  from sklearn.multiclass import OneVsRestClassifier
  from sklearn.multioutput import ClassifierChain, MultiOutputClassifier, MultiOutputRegressor, RegressorChain
  from sklearn.pipeline import make_pipeline
  from sklearn.svm import LinearSVC
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.utils import shuffle
  from sklearn.utils._testing import assert_almost_equal, assert_array_almost_equal, assert_array_equal
  from sklearn.utils.fixes import BSR_CONTAINERS, COO_CONTAINERS, CSC_CONTAINERS, CSR_CONTAINERS, DOK_CONTAINERS, LIL_CONTAINERS
label_desc: Use image node_id 23 for calling a global function with example usage: test_classifier_chain_fit_and_predict_with_sparse_data(csr_container) without return types