code: def test_rf_get_text(n_estimators, detailed_text):
      X, y = make_classification(
          n_samples=500,
          n_features=10,
          n_clusters_per_class=1,
          n_informative=5,
          random_state=94929,
          n_classes=2,
      )
  
      X = X.astype(np.float32)
      y = y.astype(np.int32)
  
      # Create a handle for the cuml model
      handle, stream = get_handle(True, n_streams=1)
  
      # Initialize cuML Random Forest classification model
      cuml_model = curfc(
          handle=handle,
          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=16,
      )
  
      # Train model on the data
      cuml_model.fit(X, y)
  
      if detailed_text:
          text_output = cuml_model.get_detailed_text()
      else:
          text_output = cuml_model.get_summary_text()
  
      # Test 1: Output is non-zero
      assert "" != text_output
  
      # Count the number of trees printed
      tree_count = 0
      for line in text_output.split("\n"):
          if line.strip().startswith("Tree #"):
              tree_count += 1
  
      # Test 2: Correct number of trees are printed
      assert n_estimators == tree_count
  
imports: import treelite
  from sklearn.model_selection import train_test_split
  from sklearn.datasets import fetch_california_housing, make_classification, make_regression, load_iris, load_breast_cancer
  from sklearn.metrics import accuracy_score, mean_squared_error, mean_tweedie_deviance
  from sklearn.ensemble import RandomForestRegressor
  from sklearn.ensemble import RandomForestClassifier
  import cuml.internals.logger
  from cuml.testing.utils import get_handle, unit_param, quality_param, stress_param
  from cuml.metrics import r2_score
  from cuml.ensemble import RandomForestRegressor
  from cuml.ensemble import RandomForestClassifier
  import cuml
  from cuml.internals.safe_imports import gpu_only_import_from
  import os
  import json
  import random
  from cuml.internals.safe_imports import cpu_only_import
  import pytest
  import warnings
  from cuml.internals.safe_imports import gpu_only_import
label_desc: Use image node_id 23 for calling a global function with example usage: test_rf_get_text(n_estimators, detailed_text) without return types