code: def test_sparse_realdata(csr_container):
      # Test on a subset from the 20newsgroups dataset.
      # This catches some bugs if input is not correctly converted into
      # sparse format or weights are not correctly initialized.
      data = np.array([0.03771744, 0.1003567, 0.01174647, 0.027069])
  
      # SVC does not support large sparse, so we specify int32 indices
      # In this case, `csr_matrix` automatically uses int32 regardless of the dtypes of
      # `indices` and `indptr` but `csr_array` may or may not use the same dtype as
      # `indices` and `indptr`, which would be int64 if not specified
      indices = np.array([6, 5, 35, 31], dtype=np.int32)
      indptr = np.array(
          [0] * 8 + [1] * 32 + [2] * 38 + [4] * 3, dtype=np.int32
      )
  
      X = csr_container((data, indices, indptr))
      y = np.array(
          [
              1.0,
              0.0,
              2.0,
              2.0,
              1.0,
              1.0,
              1.0,
              2.0,
              2.0,
              0.0,
              1.0,
              2.0,
              2.0,
              0.0,
              2.0,
              0.0,
              3.0,
              0.0,
              3.0,
              0.0,
              1.0,
              1.0,
              3.0,
              2.0,
              3.0,
              2.0,
              0.0,
              3.0,
              1.0,
              0.0,
              2.0,
              1.0,
              2.0,
              0.0,
              1.0,
              0.0,
              2.0,
              3.0,
              1.0,
              3.0,
              0.0,
              1.0,
              0.0,
              0.0,
              2.0,
              0.0,
              1.0,
              2.0,
              2.0,
              2.0,
              3.0,
              2.0,
              0.0,
              3.0,
              2.0,
              1.0,
              2.0,
              3.0,
              2.0,
              2.0,
              0.0,
              1.0,
              0.0,
              1.0,
              2.0,
              3.0,
              0.0,
              0.0,
              2.0,
              2.0,
              1.0,
              3.0,
              1.0,
              1.0,
              0.0,
              1.0,
              2.0,
              1.0,
              1.0,
              3.0,
          ]
      )
  
      clf = svm.SVC(kernel="linear").fit(X.toarray(), y)
      sp_clf = svm.SVC(kernel="linear").fit(X.tocoo(), y)
  
      assert_array_equal(
          clf.support_vectors_, sp_clf.support_vectors_.toarray()
      )
      assert_array_equal(clf.dual_coef_, sp_clf.dual_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 14 for calling a global function with example usage: test_sparse_realdata(csr_container) without return types