code: def test_model_init(
      data: Dataset,
      lags,
      order,
      trend,
      causal,
      fixed,
      use_numpy,
      seasonal,
  ):
      y, x, z, order, period = _convert_to_numpy(
          data, fixed, order, seasonal, use_numpy
      )
  
      mod = ARDL(
          y,
          lags,
          x,
          order,
          trend,
          causal=causal,
          fixed=z,
          seasonal=seasonal,
          period=period,
      )
      res = mod.fit()
      check_results(res)
      res.predict()
      fixed_oos = None
      if z is not None:
          fixed_oos = np.array(z)[:12]
          if isinstance(z, pd.DataFrame):
              fixed_oos = pd.DataFrame(fixed_oos, columns=z.columns)
      exog_oos = None
      if x is not None:
          exog_oos = np.array(x)[:12]
          if isinstance(x, pd.DataFrame):
              exog_oos = pd.DataFrame(exog_oos, columns=x.columns)
  
      res.forecast(12, exog=exog_oos, fixed=fixed_oos)
  
imports: from typing import NamedTuple
  import numpy
  from numpy.testing import assert_allclose
  import pandas
  from pandas.testing import assert_index_equal
  import pytest
  from statsmodels.datasets import danish_data
  from statsmodels.iolib.summary import Summary
  from statsmodels.tools.sm_exceptions import SpecificationWarning
  from statsmodels.tsa.ar_model import AutoReg
  from statsmodels.tsa.ardl.model import ARDL, UECM, ARDLResults, ardl_select_order
  from statsmodels.tsa.deterministic import DeterministicProcess
label_desc: Use image node_id 16 for calling a global function with example usage: test_model_init(data, lags, order, trend, causal, fixed, use_numpy, seasonal) without return types