code: def test_linearsvc(lil_container, dok_container):
      # Similar to test_SVC
      X_sp = lil_container(X)
      X2_sp = dok_container(X2)
  
      clf = svm.LinearSVC(dual="auto", random_state=0).fit(X, Y)
      sp_clf = svm.LinearSVC(dual="auto", random_state=0).fit(X_sp, Y)
  
      assert sp_clf.fit_intercept
  
      assert_array_almost_equal(clf.coef_, sp_clf.coef_, decimal=4)
      assert_array_almost_equal(
          clf.intercept_, sp_clf.intercept_, decimal=4
      )
  
      assert_allclose(clf.predict(X), sp_clf.predict(X_sp))
  
      clf.fit(X2, Y2)
      sp_clf.fit(X2_sp, Y2)
  
      assert_array_almost_equal(clf.coef_, sp_clf.coef_, decimal=4)
      assert_array_almost_equal(
          clf.intercept_, sp_clf.intercept_, decimal=4
      )
  
imports: import numpy
  import pytest
  from scipy import sparse
  from sklearn import base, datasets, linear_model, svm
  from sklearn.datasets import load_digits, make_blobs, make_classification
  from sklearn.exceptions import ConvergenceWarning
  from sklearn.svm.tests import test_svm
  from sklearn.utils._testing import assert_allclose, assert_array_almost_equal, assert_array_equal, ignore_warnings, skip_if_32bit
  from sklearn.utils.extmath import safe_sparse_dot
  from sklearn.utils.fixes import CSR_CONTAINERS, DOK_CONTAINERS, LIL_CONTAINERS
label_desc: Use image node_id 8 for calling a global function with example usage: test_linearsvc(lil_container, dok_container) without return types