code: def test_regression_random_forest():
      estimator = lgb.LGBMRegressor(
          boosting_type="rf",
          n_estimators=2,
          random_state=1,
          max_depth=1,
          subsample=0.7,
          subsample_freq=1,
      )
      utils.get_regression_model_trainer()(estimator)
  
      assembler = LightGBMModelAssembler(estimator)
      actual = assembler.assemble()
  
      expected = ast.BinNumExpr(
          ast.BinNumExpr(
              ast.IfExpr(
                  ast.CompExpr(
                      ast.FeatureRef(12),
                      ast.NumVal(9.605),
                      ast.CompOpType.GT,
                  ),
                  ast.NumVal(17.398543657369768),
                  ast.NumVal(29.851408659650296),
              ),
              ast.IfExpr(
                  ast.CompExpr(
                      ast.FeatureRef(5),
                      ast.NumVal(6.888),
                      ast.CompOpType.GT,
                  ),
                  ast.NumVal(37.2235298136268),
                  ast.NumVal(19.948122884684025),
              ),
              ast.BinNumOpType.ADD,
          ),
          ast.NumVal(0.5),
          ast.BinNumOpType.MUL,
      )
  
      assert utils.cmp_exprs(actual, expected)
  
file_path: m2cgen/tests/assemblers/test_boosting_lightgbm.py
imports: import lightgbm
  import numpy
  import pytest
  from m2cgen import ast
  from m2cgen.assemblers import LightGBMModelAssembler
  from tests import utils
label_desc: Use image node_id 4 for calling a global function with example usage: test_regression_random_forest() without return types