code: def generate_exog_from_season(seasons, endog_len):
      """
      Translate seasons to exog matrix.
  
      Parameters
      ----------
      seasons : int
          Number of seasons.
      endog_len : int
          Number of observations.
  
      Returns
      -------
      exog : ndarray or None
          If seasonal deterministic terms exist, the corresponding exog-matrix is
          returned.
          Otherwise, None is returned.
      """
  
      exog_stack = []
      if seasons > 0:
          season_exog = np.zeros((seasons - 1, endog_len))
          for i in range(seasons - 1):
              season_exog[i, i::seasons] = 1
          # season_exog = season_exog[:, ::-1]
          # season_exog = np.hstack((season_exog[:, 3:4],
          #   season_exog[:, :-1]))
          # season_exog = np.hstack((season_exog[:, 2:4],
          #                          season_exog[:, :-2]))
          # season_exog = np.hstack((season_exog[:, 1:4], season_exog[:, :-3]))
          # season_exog[1] = -season_exog[1]
          # the following line is commented out because seasonal terms are
          # *not* centered in JMulTi's VAR-framework (in contrast to VECM)
          # season_exog -= 1 / seasons
          season_exog = season_exog.T
          exog_stack.append(season_exog)
      if exog_stack != []:
          exog = np.column_stack(exog_stack)
      else:
          exog = None
      return exog
  
imports: import numpy
  from numpy.testing import assert_, assert_allclose, assert_raises
  import statsmodels.datasets.macrodata.data
  from statsmodels.tsa.vector_ar.tests.JMulTi_results.parse_jmulti_vecm_output import sublists
  from statsmodels.tsa.vector_ar.var_model import VAR
  from .JMulTi_results.parse_jmulti_var_output import dt_s_tup_to_string, load_results_jmulti
label_desc: Use image node_id 3 for calling a global function with example usage: generate_exog_from_season(seasons, endog_len) and returns: exog