code: def test_regression_saved_without_feature_names():
      base_score = 0.6
      estimator = xgb.XGBRegressor(
          n_estimators=2,
          random_state=1,
          max_depth=1,
          base_score=base_score,
      )
      utils.get_regression_model_trainer()(estimator)
  
      with utils.tmp_dir() as tmp_dirpath:
          filename = tmp_dirpath / "tmp.file"
          estimator.save_model(filename)
          estimator = xgb.XGBRegressor(base_score=base_score)
          estimator.load_model(filename)
  
      assembler = XGBoostModelAssemblerSelector(estimator)
      actual = assembler.assemble()
  
      expected = ast.BinNumExpr(
          ast.NumVal(base_score),
          ast.BinNumExpr(
              ast.IfExpr(
                  ast.CompExpr(
                      ast.FeatureRef(12),
                      ast.NumVal(9.72500038),
                      ast.CompOpType.GTE,
                  ),
                  ast.NumVal(4.995625019073486),
                  ast.NumVal(8.715502738952637),
              ),
              ast.IfExpr(
                  ast.CompExpr(
                      ast.FeatureRef(5),
                      ast.NumVal(6.94099998),
                      ast.CompOpType.GTE,
                  ),
                  ast.NumVal(8.309040069580078),
                  ast.NumVal(3.930694580078125),
              ),
              ast.BinNumOpType.ADD,
          ),
          ast.BinNumOpType.ADD,
      )
  
      assert utils.cmp_exprs(actual, expected)
  
file_path: m2cgen/tests/assemblers/test_boosting_xgboost.py
imports: import numpy
  import xgboost
  from m2cgen import ast
  from m2cgen.assemblers import XGBoostModelAssemblerSelector
  from m2cgen.assemblers.utils import apply_op_to_expressions
  from tests import utils
label_desc: Use image node_id 6 for calling a global function with example usage: test_regression_saved_without_feature_names() without return types