code: def test_svm_skl_cmp_predict_proba(in_type, n_rows=10000, n_cols=20):
      params = {
          "kernel": "rbf",
          "C": 1,
          "tol": 1e-3,
          "gamma": "scale",
          "probability": True,
      }
      X, y = make_classification(
          n_samples=n_rows,
          n_features=n_cols,
          n_informative=2,
          n_redundant=10,
          random_state=137,
      )
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, test_size=0.8, random_state=42
      )
  
      X_m = input_to_cuml_array(X_train).array
      y_m = input_to_cuml_array(y_train).array
  
      cuSVC = cu_svm.SVC(**params)
      cuSVC.fit(X_m.to_output(in_type), y_m.to_output(in_type))
      sklSVC = svm.SVC(**params)
      sklSVC.fit(X_train, y_train)
      compare_probabilistic_svm(
          cuSVC, sklSVC, X_test, y_test, 1e-3, 1e-2
      )
  
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 8 for calling a global function with example usage: test_svm_skl_cmp_predict_proba(in_type, n_rows, n_cols) without return types