code: def test_multiple_fits_classification(
      large_clf, n_estimators, n_bins
  ):
      datatype = np.float32
      X, y = large_clf
      X = X.astype(datatype)
      y = y.astype(np.int32)
      cuml_model = curfc(
          n_bins=n_bins, n_estimators=n_estimators, max_depth=10
      )
  
      # Calling multiple fits
      cuml_model.fit(X, y)
  
      cuml_model.fit(X, y)
  
      # Check if params are still intact
      params = cuml_model.get_params()
      assert params["n_estimators"] == n_estimators
      assert params["n_bins"] == n_bins
  
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 21 for calling a global function with example usage: test_multiple_fits_classification(large_clf, n_estimators, n_bins) without return types