code: def test_rf_classification_sparse(
      small_clf, datatype, fil_sparse_format, algo
  ):
      use_handle = True
      num_treees = 50
  
      X, y = small_clf
      X = X.astype(datatype)
      y = y.astype(np.int32)
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, train_size=0.8, random_state=0
      )
      # Create a handle for the cuml model
      handle, stream = get_handle(use_handle, n_streams=1)
  
      # Initialize, fit and predict using cuML's
      # random forest classification model
      cuml_model = curfc(
          n_bins=16,
          split_criterion=0,
          min_samples_leaf=2,
          random_state=123,
          n_streams=1,
          n_estimators=num_treees,
          handle=handle,
          max_leaves=-1,
          max_depth=40,
      )
      cuml_model.fit(X_train, y_train)
  
      if (
          not fil_sparse_format
          or algo == "tree_reorg"
          or algo == "batch_tree_reorg"
      ) or fil_sparse_format == "not_supported":
          with pytest.raises(ValueError):
              fil_preds = cuml_model.predict(
                  X_test,
                  predict_model="GPU",
                  threshold=0.5,
                  fil_sparse_format=fil_sparse_format,
                  algo=algo,
              )
      else:
          fil_preds = cuml_model.predict(
              X_test,
              predict_model="GPU",
              threshold=0.5,
              fil_sparse_format=fil_sparse_format,
              algo=algo,
          )
          fil_preds = np.reshape(fil_preds, np.shape(y_test))
          fil_acc = accuracy_score(y_test, fil_preds)
          np.testing.assert_almost_equal(
              fil_acc, cuml_model.score(X_test, y_test)
          )
  
          fil_model = cuml_model.convert_to_fil_model()
  
          with cuml.using_output_type("numpy"):
              fil_model_preds = fil_model.predict(X_test)
              fil_model_acc = accuracy_score(y_test, fil_model_preds)
              assert fil_acc == fil_model_acc
  
          tl_model = cuml_model.convert_to_treelite_model()
          assert num_treees == tl_model.num_trees
          assert X.shape[1] == tl_model.num_features
  
          if X.shape[0] < 500000:
              sk_model = skrfc(
                  n_estimators=50,
                  max_depth=40,
                  min_samples_split=2,
                  random_state=10,
              )
              sk_model.fit(X_train, y_train)
              sk_preds = sk_model.predict(X_test)
              sk_acc = accuracy_score(y_test, sk_preds)
              assert fil_acc >= (sk_acc - 0.07)
  
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 17 for calling a global function with example usage: test_rf_classification_sparse(small_clf, datatype, fil_sparse_format, algo) without return types