code: def test_rf_get_json(client, estimator_type, max_depth, n_estimators):
      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(
          n_samples=350,
          n_features=20,
          n_clusters_per_class=1,
          n_informative=10,
          random_state=123,
          n_classes=2,
      )
      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,
              split_criterion=0,
              min_samples_leaf=2,
              random_state=23707,
              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,
              min_samples_leaf=2,
              random_state=23707,
              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)
      json_out = cu_rf_mg.get_json()
      json_obj = json.loads(json_out)
  
      # Test 1: Output is non-zero
      assert "" != json_out
  
      # Test 2: JSON object contains correct number of trees
      assert isinstance(json_obj, list)
      assert len(json_obj) == n_estimators
  
      # Test 3: Traverse JSON trees and get the same predictions as cuML RF
      def predict_with_json_tree(tree, x):
          if "children" not in tree:
              assert "leaf_value" in tree
              return tree["leaf_value"]
          assert "split_feature" in tree
          assert "split_threshold" in tree
          assert "yes" in tree
          assert "no" in tree
          if x[tree["split_feature"]] <= tree["split_threshold"] + 1e-5:
              return predict_with_json_tree(tree["children"][0], x)
          return predict_with_json_tree(tree["children"][1], x)
  
      def predict_with_json_rf_classifier(rf, x):
          # Returns the class with the highest vote. If there is a tie, return
          # the list of all classes with the highest vote.
          predictions = []
          for tree in rf:
              predictions.append(
                  np.array(predict_with_json_tree(tree, x))
              )
          predictions = np.sum(predictions, axis=0)
          return np.argmax(predictions)
  
      def predict_with_json_rf_regressor(rf, x):
          pred = 0.0
          for tree in rf:
              pred += predict_with_json_tree(tree, x)[0]
          return pred / len(rf)
  
      if estimator_type == "classification":
          expected_pred = cu_rf_mg.predict(X_dask).astype(np.int32)
          expected_pred = expected_pred.compute().to_numpy()
          for idx, row in enumerate(X):
              majority_vote = predict_with_json_rf_classifier(
                  json_obj, row
              )
              assert expected_pred[idx] == majority_vote
      elif estimator_type == "regression":
          expected_pred = cu_rf_mg.predict(X_dask).astype(np.float32)
          expected_pred = expected_pred.compute().to_numpy()
          pred = []
          for idx, row in enumerate(X):
              pred.append(predict_with_json_rf_regressor(json_obj, row))
          pred = np.array(pred, dtype=np.float32)
          np.testing.assert_almost_equal(pred, expected_pred, decimal=6)
  
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 9 for calling a global function with example usage: test_rf_get_json(client, estimator_type, max_depth, n_estimators) and returns: predict_with_json_tree, np, unknown, tree, predict_with_json_tree