code: def test_svm_skl_cmp_multiclass(
      params, sparse, dataset="classification2", n_rows=100, n_cols=6
  ):
      X_train, X_test, y_train, y_test = make_dataset(
          dataset, n_rows, n_cols, n_classes=3, n_informative=6
      )
  
      if sparse:
          X_train = scipy_sparse.csr_matrix(X_train)
          X_test = scipy_sparse.csr_matrix(X_test)
  
      # Default to numpy for testing
      with cuml.using_output_type("numpy"):
          cuSVC = cu_svm.SVC(**params)
          cuSVC.fit(X_train, y_train)
  
          sklSVC = svm.SVC(**params)
          sklSVC.fit(X_train, y_train)
  
          compare_svm(
              cuSVC,
              sklSVC,
              X_test,
              y_test,
              coef_tol=1e-5,
              report_summary=True,
          )
  
imports: import platform
  from sklearn.preprocessing import StandardScaler
  from sklearn.model_selection import train_test_split
  from sklearn.metrics import mean_squared_error
  from sklearn.datasets import make_classification, make_gaussian_quantiles
  from sklearn.datasets import make_regression, make_friedman1
  from sklearn.datasets import load_iris, make_blobs
  from sklearn import svm
  from cuml.testing.utils import unit_param, quality_param, stress_param, compare_svm, compare_probabilistic_svm, svm_array_equal
  from cuml.common import input_to_cuml_array
  import cuml.svm
  import cuml
  from cuml.internals.safe_imports import gpu_only_import_from
  from cuml.internals.safe_imports import cpu_only_import
  import pytest
  from cuml.internals.safe_imports import gpu_only_import
label_desc: Use image node_id 5 for calling a global function with example usage: test_svm_skl_cmp_multiclass(params, sparse, dataset, n_rows, n_cols) without return types