code: def test_rf_get_combined_model_right_aftter_fit(
      client, estimator_type
  ):
      max_depth = 3
      n_estimators = 5
  
      n_workers = len(client.scheduler_info()["workers"])
      if n_estimators < n_workers:
          err_msg = (
              "n_estimators cannot be lower than number of dask workers"
          )
          pytest.xfail(err_msg)
  
      X, y = make_classification()
      X = X.astype(np.float32)
      if estimator_type == "classification":
          cu_rf_mg = cuRFC_mg(
              max_features=1.0,
              max_samples=1.0,
              n_bins=16,
              n_streams=1,
              n_estimators=n_estimators,
              max_leaves=-1,
              max_depth=max_depth,
          )
          y = y.astype(np.int32)
      elif estimator_type == "regression":
          cu_rf_mg = cuRFR_mg(
              max_features=1.0,
              max_samples=1.0,
              n_bins=16,
              n_streams=1,
              n_estimators=n_estimators,
              max_leaves=-1,
              max_depth=max_depth,
          )
          y = y.astype(np.float32)
      else:
          assert False
      X_dask, y_dask = _prep_training_data(
          client, X, y, partitions_per_worker=2
      )
      cu_rf_mg.fit(X_dask, y_dask)
      single_gpu_model = cu_rf_mg.get_combined_model()
      if estimator_type == "classification":
          assert isinstance(single_gpu_model, cuRFC_sg)
      elif estimator_type == "regression":
          assert isinstance(single_gpu_model, cuRFR_sg)
      else:
          assert False
  
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 11 for calling a global function with example usage: test_rf_get_combined_model_right_aftter_fit(client, estimator_type) without return types