code: def test_rf_concatenation_dask(client, model_type):
      n_workers = len(client.scheduler_info()["workers"])
  
      from cuml.fil.fil import TreeliteModel
  
      X, y = make_classification(
          n_samples=n_workers * 200,
          n_features=30,
          random_state=123,
          n_classes=2,
      )
  
      X = X.astype(np.float32)
      if model_type == "classification":
          y = y.astype(np.int32)
      else:
          y = y.astype(np.float32)
      n_estimators = 40
      cu_rf_params = {"n_estimators": n_estimators}
  
      X_df, y_df = _prep_training_data(
          client, X, y, partitions_per_worker=2
      )
  
      if model_type == "classification":
          cu_rf_mg = cuRFC_mg(**cu_rf_params)
      else:
          cu_rf_mg = cuRFR_mg(**cu_rf_params)
  
      cu_rf_mg.fit(X_df, y_df)
      res1 = cu_rf_mg.predict(X_df)
      res1.compute()
      if cu_rf_mg.internal_model:
          local_tl = TreeliteModel.from_treelite_model_handle(
              cu_rf_mg.internal_model._obtain_treelite_handle(),
              take_handle_ownership=False,
          )
  
          assert local_tl.num_trees == n_estimators
  
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 7 for calling a global function with example usage: test_rf_concatenation_dask(client, model_type) without return types