code: def test_sparse_oneclasssvm(
      X_train, y_train, X_test, kernel, sparse_container
  ):
      # Check that sparse OneClassSVM gives the same result as dense OneClassSVM
      X_train = sparse_container(X_train)
  
      clf = svm.OneClassSVM(gamma=1, kernel=kernel)
      check_svm_model_equal(clf, X_train, y_train, X_test)
  
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 13 for calling a global function with example usage: test_sparse_oneclasssvm(X_train, y_train, X_test, kernel, sparse_container) without return types