code: def test_fil_regression(
      n_rows, n_columns, num_rounds, tmp_path, max_depth
  ):
      # settings
      classification = False  # change this to false to use regression
      n_rows = n_rows  # we'll use 1 millions rows
      n_columns = n_columns
      random_state = np.random.RandomState(43210)
  
      X, y = simulate_data(
          n_rows,
          n_columns,
          random_state=random_state,
          classification=classification,
          bias=10.0,
      )
      # identify shape and indices
      n_rows, n_columns = X.shape
      train_size = 0.80
  
      X_train, X_validation, y_train, y_validation = train_test_split(
          X, y, train_size=train_size, random_state=0
      )
  
      model_path = os.path.join(tmp_path, "xgb_reg.model")
      bst = _build_and_save_xgboost(
          model_path,
          X_train,
          y_train,
          classification=classification,
          num_rounds=num_rounds,
          xgboost_params={"max_depth": max_depth},
      )
  
      dvalidation = xgb.DMatrix(X_validation, label=y_validation)
      xgb_preds = bst.predict(dvalidation)
  
      xgb_mse = mean_squared_error(y_validation, xgb_preds)
      fm = ForestInference.load(
          model_path, algo="auto", output_class=False
      )
      fil_preds = np.asarray(fm.predict(X_validation))
      fil_preds = np.reshape(fil_preds, np.shape(xgb_preds))
      fil_mse = mean_squared_error(y_validation, fil_preds)
  
      assert fil_mse == pytest.approx(xgb_mse, abs=0.01)
      assert np.allclose(fil_preds, xgb_preds, 1e-3)
  
imports: from sklearn.model_selection import train_test_split
  from sklearn.metrics import accuracy_score, mean_squared_error
  from sklearn.ensemble import GradientBoostingClassifier, GradientBoostingRegressor, RandomForestClassifier, RandomForestRegressor, ExtraTreesClassifier, ExtraTreesRegressor
  from sklearn.datasets import make_classification, make_regression
  from cuml.internals.import_utils import has_xgboost
  from cuml.testing.utils import array_equal, unit_param, quality_param, stress_param
  from cuml import ForestInference
  from math import ceil
  import os
  import pytest
  from cuml.internals.safe_imports import cpu_only_import
label_desc: Use image node_id 4 for calling a global function with example usage: test_fil_regression(n_rows, n_columns, num_rounds, tmp_path, max_depth) without return types