code: def test_rf_classification_dask_array(partitions_per_worker, client):
      n_workers = len(client.scheduler_info()["workers"])
  
      X, y = make_classification(
          n_samples=n_workers * 2000,
          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 * 400
      )
  
      cu_rf_params = {
          "n_estimators": 25,
          "max_depth": 13,
          "n_bins": 15,
      }
  
      X_train_df, y_train_df = _prep_training_data(
          client, X_train, y_train, partitions_per_worker
      )
      X_test_dask_array = from_array(X_test)
      cuml_mod = cuRFC_mg(**cu_rf_params)
      cuml_mod.fit(X_train_df, y_train_df)
      cuml_mod_predict = cuml_mod.predict(X_test_dask_array).compute()
  
      acc_score = accuracy_score(
          cuml_mod_predict, y_test, normalize=True
      )
  
      assert acc_score > 0.8
  
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 4 for calling a global function with example usage: test_rf_classification_dask_array(partitions_per_worker, client) without return types