code: def test_rf_instance_count(client, 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)
  
      n_samples_per_worker = 350
  
      X, y = make_classification(
          n_samples=n_samples_per_worker * n_workers,
          n_features=20,
          n_clusters_per_class=1,
          n_informative=10,
          random_state=123,
          n_classes=2,
      )
      X = X.astype(np.float32)
      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)
  
      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)
  
      # The instance count of each node must be equal to the sum of
      # the instance counts of its children
      def check_instance_count_for_non_leaf(tree):
          assert "instance_count" in tree
          if "children" not in tree:
              return
          assert "instance_count" in tree["children"][0]
          assert "instance_count" in tree["children"][1]
          assert (
              tree["instance_count"]
              == tree["children"][0]["instance_count"]
              + tree["children"][1]["instance_count"]
          )
          check_instance_count_for_non_leaf(tree["children"][0])
          check_instance_count_for_non_leaf(tree["children"][1])
  
      for tree in json_obj:
          check_instance_count_for_non_leaf(tree)
          # The root's count should be equal to the number of rows in the data
          assert tree["instance_count"] == n_samples_per_worker
  
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 10 for calling a global function with example usage: test_rf_instance_count(client, max_depth, n_estimators) without return types