code: def test_rf_nbins_small(small_clf):
      X, y = small_clf
      X = X.astype(np.float32)
      y = y.astype(np.int32)
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, train_size=0.8, random_state=0
      )
      # Initialize, fit and predict using cuML's
      # random forest classification model
      cuml_model = curfc()
  
      # display warning when nbins less than samples
      with warnings.catch_warnings(record=True) as w:
          warnings.simplefilter("always")
          cuml_model.fit(X_train[0:3, :], y_train[0:3])
          assert (
              "The number of bins, `n_bins` is greater than "
              "the number of samples used for training. "
              "Changing `n_bins` to number of training samples."
              in str(w[-1].message)
          )
  
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 27 for calling a global function with example usage: test_rf_nbins_small(small_clf) without return types