code: def test_processmle():
      estimator = utils.StatsmodelsSklearnLikeWrapper(
          ProcessMLE,
          dict(
              init=dict(
                  exog_scale=np.ones(
                      (
                          len(
                              utils.get_regression_model_trainer().y_train
                          ),
                          2,
                      )
                  ),
                  exog_smooth=np.ones(
                      (
                          len(
                              utils.get_regression_model_trainer().y_train
                          ),
                          2,
                      )
                  ),
                  exog_noise=np.ones(
                      (
                          len(
                              utils.get_regression_model_trainer().y_train
                          ),
                          2,
                      )
                  ),
                  time=np.kron(
                      np.ones(
                          len(
                              utils.get_regression_model_trainer().y_train
                          )
                          // 3
                      ),
                      np.arange(3),
                  ),
                  groups=np.kron(
                      np.arange(
                          len(
                              utils.get_regression_model_trainer().y_train
                          )
                          // 3
                      ),
                      np.ones(3),
                  ),
              ),
              fit=dict(maxiter=1),
          ),
      )
      _, __, estimator = utils.get_regression_model_trainer()(estimator)
  
      assembler = assemblers.ProcessMLEModelAssembler(estimator)
      actual = assembler.assemble()
  
      feature_weight_mul = [
          ast.BinNumExpr(
              ast.FeatureRef(0),
              ast.NumVal(-0.0980302102110356),
              ast.BinNumOpType.MUL,
          ),
          ast.BinNumExpr(
              ast.FeatureRef(1),
              ast.NumVal(0.04863869398287732),
              ast.BinNumOpType.MUL,
          ),
          ast.BinNumExpr(
              ast.FeatureRef(2),
              ast.NumVal(0.009514054355147874),
              ast.BinNumOpType.MUL,
          ),
          ast.BinNumExpr(
              ast.FeatureRef(3),
              ast.NumVal(2.977113829322681),
              ast.BinNumOpType.MUL,
          ),
          ast.BinNumExpr(
              ast.FeatureRef(4),
              ast.NumVal(-2.6048073854474705),
              ast.BinNumOpType.MUL,
          ),
          ast.BinNumExpr(
              ast.FeatureRef(5),
              ast.NumVal(5.887987153279099),
              ast.BinNumOpType.MUL,
          ),
          ast.BinNumExpr(
              ast.FeatureRef(6),
              ast.NumVal(-0.008183580358672775),
              ast.BinNumOpType.MUL,
          ),
          ast.BinNumExpr(
              ast.FeatureRef(7),
              ast.NumVal(-0.996428929917054),
              ast.BinNumOpType.MUL,
          ),
          ast.BinNumExpr(
              ast.FeatureRef(8),
              ast.NumVal(0.1618353156581333),
              ast.BinNumOpType.MUL,
          ),
          ast.BinNumExpr(
              ast.FeatureRef(9),
              ast.NumVal(-0.009213049690188308),
              ast.BinNumOpType.MUL,
          ),
          ast.BinNumExpr(
              ast.FeatureRef(10),
              ast.NumVal(-0.3634816838591863),
              ast.BinNumOpType.MUL,
          ),
          ast.BinNumExpr(
              ast.FeatureRef(11),
              ast.NumVal(0.014700492832969888),
              ast.BinNumOpType.MUL,
          ),
          ast.BinNumExpr(
              ast.FeatureRef(12),
              ast.NumVal(-0.4384298738156768),
              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 5 for calling a global function with example usage: test_processmle() without return types