code: def test_svm_skl_cmp_decision_function(
      params, n_rows=4000, n_cols=20
  ):
      X_train, X_test, y_train, y_test = make_dataset(
          "classification1", n_rows, n_cols
      )
      y_train = y_train.astype(np.int32)
      y_test = y_test.astype(np.int32)
  
      cuSVC = cu_svm.SVC(**params)
      cuSVC.fit(X_train, y_train)
  
      pred = cuSVC.predict(X_test)
      assert pred.dtype == y_train.dtype
  
      df1 = cuSVC.decision_function(X_test)
      assert df1.dtype == X_train.dtype
  
      sklSVC = svm.SVC(**params)
      sklSVC.fit(X_train, y_train)
      df2 = sklSVC.decision_function(X_test)
  
      if params["probability"]:
          tol = 2e-2  # See comments in SVC decision_function method
      else:
          tol = 1e-5
      assert mean_squared_error(df1, df2) < tol
  
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 6 for calling a global function with example usage: test_svm_skl_cmp_decision_function(params, n_rows, n_cols) without return types