code: def load_results_statsmodels(dataset):
      results_per_deterministic_terms = dict.fromkeys(dataset.dt_s_list)
  
      for dt_s_tup in dataset.dt_s_list:
          model = VECM(
              data[dataset],
              k_ar_diff=3,
              coint_rank=coint_rank,
              deterministic=dt_s_tup[0],
              seasons=dt_s_tup[1],
              first_season=dt_s_tup[2],
          )
          results_per_deterministic_terms[dt_s_tup] = model.fit(
              method="ml"
          )
      return results_per_deterministic_terms
  
imports: import numpy
  from numpy.testing import assert_, assert_allclose, assert_array_equal, assert_raises, assert_raises_regex
  import pytest
  import statsmodels.datasets.interest_inflation.data
  from statsmodels.tools.testing import assert_equal
  from statsmodels.tsa.vector_ar.tests.JMulTi_results.parse_jmulti_vecm_output import dt_s_tup_to_string, load_results_jmulti, sublists
  from statsmodels.tsa.vector_ar.util import seasonal_dummies
  from statsmodels.tsa.vector_ar.var_model import VARProcess
  from statsmodels.tsa.vector_ar.vecm import VECM, select_coint_rank, select_order
label_desc: Use image node_id 2 for calling a global function with example usage: load_results_statsmodels(dataset) and returns: results_per_deterministic_terms