code: def test_svc_iris(csr_container, kernel):
      # Test the sparse SVC with the iris dataset
      iris_data_sp = csr_container(iris.data)
  
      sp_clf = svm.SVC(kernel=kernel).fit(iris_data_sp, iris.target)
      clf = svm.SVC(kernel=kernel).fit(iris.data, iris.target)
  
      assert_allclose(
          clf.support_vectors_, sp_clf.support_vectors_.toarray()
      )
      assert_allclose(clf.dual_coef_, sp_clf.dual_coef_.toarray())
      assert_allclose(
          clf.predict(iris.data), sp_clf.predict(iris_data_sp)
      )
      if kernel == "linear":
          assert_allclose(clf.coef_, sp_clf.coef_.toarray())
  
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 5 for calling a global function with example usage: test_svc_iris(csr_container, kernel) without return types