code: def test_rf_regression_dask_fil(partitions_per_worker, dtype, client):
      n_workers = len(client.scheduler_info()["workers"])
  
      # Use CUDA_VISIBLE_DEVICES to control the number of workers
      X, y = make_regression(
          n_samples=n_workers * 4000,
          n_features=20,
          n_informative=10,
          random_state=123,
      )
  
      X = X.astype(dtype)
      y = y.astype(dtype)
  
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, test_size=n_workers * 100, random_state=123
      )
  
      if dtype == np.float64:
          pytest.xfail(
              reason=" Dask RF does not support np.float64 data"
          )
  
      cu_rf_params = {
          "n_estimators": 50,
          "max_depth": 16,
          "n_bins": 16,
      }
  
      workers = client.has_what().keys()
      n_partitions = partitions_per_worker * len(workers)
  
      X_cudf = cudf.DataFrame.from_pandas(pd.DataFrame(X_train))
      X_train_df = dask_cudf.from_cudf(X_cudf, npartitions=n_partitions)
  
      y_cudf = cudf.Series(y_train)
      y_train_df = dask_cudf.from_cudf(y_cudf, npartitions=n_partitions)
      X_cudf_test = cudf.DataFrame.from_pandas(pd.DataFrame(X_test))
      X_test_df = dask_cudf.from_cudf(
          X_cudf_test, npartitions=n_partitions
      )
  
      cuml_mod = cuRFR_mg(**cu_rf_params, ignore_empty_partitions=True)
      cuml_mod.fit(X_train_df, y_train_df)
  
      cuml_mod_predict = cuml_mod.predict(X_test_df)
      cuml_mod_predict = cp.asnumpy(
          cp.array(cuml_mod_predict.compute())
      )
  
      acc_score = r2_score(cuml_mod_predict, y_test)
  
      assert acc_score >= 0.59
  
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 3 for calling a global function with example usage: test_rf_regression_dask_fil(partitions_per_worker, dtype, client) without return types