code: def test_rf_classification_dask_fil_predict_proba(
      partitions_per_worker, client
  ):
      n_workers = len(client.scheduler_info()["workers"])
  
      X, y = make_classification(
          n_samples=n_workers * 1500,
          n_features=30,
          n_clusters_per_class=1,
          n_informative=20,
          random_state=123,
          n_classes=2,
      )
  
      X = X.astype(np.float32)
      y = y.astype(np.int32)
  
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, test_size=n_workers * 150, random_state=123
      )
  
      cu_rf_params = {
          "n_bins": 16,
          "n_streams": 1,
          "n_estimators": 40,
          "max_depth": 16,
      }
  
      X_train_df, y_train_df = _prep_training_data(
          client, X_train, y_train, partitions_per_worker
      )
      X_test_df, _ = _prep_training_data(
          client, X_test, y_test, partitions_per_worker
      )
      cu_rf_mg = cuRFC_mg(**cu_rf_params)
      cu_rf_mg.fit(X_train_df, y_train_df)
  
      fil_preds = cu_rf_mg.predict(X_test_df).compute()
      fil_preds = fil_preds.to_numpy()
      fil_preds_proba = cu_rf_mg.predict_proba(X_test_df).compute()
      fil_preds_proba = fil_preds_proba.to_numpy()
      np.testing.assert_equal(
          fil_preds, np.argmax(fil_preds_proba, axis=1)
      )
  
      y_proba = np.zeros(np.shape(fil_preds_proba))
      y_proba[:, 1] = y_test
      y_proba[:, 0] = 1.0 - y_test
      fil_mse = mean_squared_error(y_proba, fil_preds_proba)
      sk_model = skrfc(n_estimators=40, max_depth=16, random_state=10)
      sk_model.fit(X_train, y_train)
      sk_preds_proba = sk_model.predict_proba(X_test)
      sk_mse = mean_squared_error(y_proba, sk_preds_proba)
  
      # The threshold is required as the test would intermitently
      # fail with a max difference of 0.029 between the two mse values
      assert fil_mse <= sk_mse + 0.029
  
imports: from dask.distributed import Client
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.metrics import accuracy_score, r2_score, mean_squared_error
  from sklearn.model_selection import train_test_split
  from sklearn.datasets import make_regression, make_classification
  from dask.array import from_array
  from cuml.ensemble import RandomForestRegressor
  from cuml.ensemble import RandomForestClassifier
  from cuml.dask.common import utils
  from cuml.dask.ensemble import RandomForestRegressor
  from cuml.dask.ensemble import RandomForestClassifier
  from cuml.internals.safe_imports import cpu_only_import
  import json
  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_rf_classification_dask_fil_predict_proba(partitions_per_worker, client) without return types