code: def check_predict_proba(test_proba, baseline_proba, y_test, rel_err):
      y_proba = np.zeros(np.shape(baseline_proba))
      for count, _class in enumerate(y_test):
          y_proba[count, _class] = 1
      baseline_mse = mean_squared_error(y_proba, baseline_proba)
      test_mse = mean_squared_error(y_proba, test_proba)
      # using relative error is more stable when changing decision tree
      # parameters, column or class count
      assert test_mse <= baseline_mse * (1.0 + rel_err)
  
imports: import treelite
  from sklearn.model_selection import train_test_split
  from sklearn.datasets import fetch_california_housing, make_classification, make_regression, load_iris, load_breast_cancer
  from sklearn.metrics import accuracy_score, mean_squared_error, mean_tweedie_deviance
  from sklearn.ensemble import RandomForestRegressor
  from sklearn.ensemble import RandomForestClassifier
  import cuml.internals.logger
  from cuml.testing.utils import get_handle, unit_param, quality_param, stress_param
  from cuml.metrics import r2_score
  from cuml.ensemble import RandomForestRegressor
  from cuml.ensemble import RandomForestClassifier
  import cuml
  from cuml.internals.safe_imports import gpu_only_import_from
  import os
  import json
  import random
  from cuml.internals.safe_imports import cpu_only_import
  import pytest
  import warnings
  from cuml.internals.safe_imports import gpu_only_import
label_desc: Use image node_id 13 for calling a global function with example usage: check_predict_proba(test_proba, baseline_proba, y_test, rel_err) without return types