code: def test_sparse_svc_clone_with_callable_kernel(lil_container):
      # Test that the "dense_fit" is called even though we use sparse input
      # meaning that everything works fine.
      a = svm.SVC(
          C=1,
          kernel=lambda x, y: x @ y.T,
          probability=True,
          random_state=0,
      )
      b = base.clone(a)
  
      X_sp = lil_container(X)
      b.fit(X_sp, Y)
      pred = b.predict(X_sp)
      b.predict_proba(X_sp)
  
      dense_svm = svm.SVC(
          C=1,
          kernel=lambda x, y: np.dot(x, y.T),
          probability=True,
          random_state=0,
      )
      pred_dense = dense_svm.fit(X, Y).predict(X)
      assert_array_equal(pred_dense, pred)
  
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 15 for calling a global function with example usage: test_sparse_svc_clone_with_callable_kernel(lil_container) without return types