code: def test_wo_const():
      estimator = utils.StatsmodelsSklearnLikeWrapper(sm.GLS, {})
      _, __, estimator = utils.get_regression_model_trainer()(estimator)
  
      assembler = assemblers.StatsmodelsModelAssemblerSelector(
          estimator
      )
      actual = assembler.assemble()
  
      feature_weight_mul = [
          ast.BinNumExpr(
              ast.FeatureRef(0),
              ast.NumVal(-0.09519078450227643),
              ast.BinNumOpType.MUL,
          ),
          ast.BinNumExpr(
              ast.FeatureRef(1),
              ast.NumVal(0.048952926782237956),
              ast.BinNumOpType.MUL,
          ),
          ast.BinNumExpr(
              ast.FeatureRef(2),
              ast.NumVal(0.007485539189808044),
              ast.BinNumOpType.MUL,
          ),
          ast.BinNumExpr(
              ast.FeatureRef(3),
              ast.NumVal(2.7302631809978273),
              ast.BinNumOpType.MUL,
          ),
          ast.BinNumExpr(
              ast.FeatureRef(4),
              ast.NumVal(-2.5078200782168034),
              ast.BinNumOpType.MUL,
          ),
          ast.BinNumExpr(
              ast.FeatureRef(5),
              ast.NumVal(5.891794660307579),
              ast.BinNumOpType.MUL,
          ),
          ast.BinNumExpr(
              ast.FeatureRef(6),
              ast.NumVal(-0.008663096157185936),
              ast.BinNumOpType.MUL,
          ),
          ast.BinNumExpr(
              ast.FeatureRef(7),
              ast.NumVal(-0.9742684875268565),
              ast.BinNumOpType.MUL,
          ),
          ast.BinNumExpr(
              ast.FeatureRef(8),
              ast.NumVal(0.1591703441858682),
              ast.BinNumOpType.MUL,
          ),
          ast.BinNumExpr(
              ast.FeatureRef(9),
              ast.NumVal(-0.009351831548409096),
              ast.BinNumOpType.MUL,
          ),
          ast.BinNumExpr(
              ast.FeatureRef(10),
              ast.NumVal(-0.36395034626096245),
              ast.BinNumOpType.MUL,
          ),
          ast.BinNumExpr(
              ast.FeatureRef(11),
              ast.NumVal(0.014529018124980565),
              ast.BinNumOpType.MUL,
          ),
          ast.BinNumExpr(
              ast.FeatureRef(12),
              ast.NumVal(-0.437443877026267),
              ast.BinNumOpType.MUL,
          ),
      ]
  
      expected = assemblers.utils.apply_op_to_expressions(
          ast.BinNumOpType.ADD, ast.NumVal(0.0), *feature_weight_mul
      )
  
      assert utils.cmp_exprs(actual, expected)
  
imports: import numpy
  import pytest
  import statsmodels.api
  from statsmodels.regression.process_regression import ProcessMLE
  from m2cgen import assemblers, ast
  from tests import utils
label_desc: Use image node_id 1 for calling a global function with example usage: test_wo_const() without return types