code: def test_create_classification_model(
      max_features, max_depth, n_estimators, n_bins
  ):
      # random forest classification model
      cuml_model = curfc(
          max_features=max_features,
          n_bins=n_bins,
          n_estimators=n_estimators,
          max_depth=max_depth,
      )
      params = cuml_model.get_params()
      cuml_model2 = curfc()
      cuml_model2.set_params(**params)
      verfiy_params = cuml_model2.get_params()
      assert params["max_features"] == verfiy_params["max_features"]
      assert params["max_depth"] == verfiy_params["max_depth"]
      assert params["n_estimators"] == verfiy_params["n_estimators"]
      assert params["n_bins"] == verfiy_params["n_bins"]
  
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 20 for calling a global function with example usage: test_create_classification_model(max_features, max_depth, n_estimators, n_bins) without return types