code: def test_clone():
      endog = dta["infl"].iloc[:50]
      exog = np.arange(endog.shape[0])
  
      # Basic model
      check_cloned(ARIMA(endog), endog)
      check_cloned(ARIMA(endog.values), endog.values)
      # With trends
      check_cloned(ARIMA(endog, trend="c"), endog)
      check_cloned(ARIMA(endog, trend="t"), endog)
      check_cloned(ARIMA(endog, trend="ct"), endog)
      # With exog
      check_cloned(ARIMA(endog, exog=exog), endog, exog=exog)
      check_cloned(ARIMA(endog, exog=exog, trend="c"), endog, exog=exog)
      # Concentrated scale
      check_cloned(
          ARIMA(endog, exog=exog, trend="c", concentrate_scale=True),
          endog,
          exog=exog,
      )
  
      # Higher order (use a different dataset to avoid warnings about
      # non-invertible start params)
      endog = dta["realgdp"].iloc[:100]
      exog = np.arange(endog.shape[0])
      check_cloned(
          ARIMA(
              endog,
              order=(2, 1, 1),
              seasonal_order=(1, 1, 2, 4),
              exog=exog,
              trend=[0, 0, 1],
              concentrate_scale=True,
          ),
          endog,
          exog=exog,
      )
  
imports: from statsmodels.compat.platform import PLATFORM_WIN32
  import io
  import numpy
  import pandas
  import pytest
  from numpy.testing import assert_equal, assert_allclose, assert_raises, assert_
  from statsmodels.datasets import macrodata
  from statsmodels.tsa.arima.model import ARIMA
  from statsmodels.tsa.arima.estimators.yule_walker import yule_walker
  from statsmodels.tsa.arima.estimators.burg import burg
  from statsmodels.tsa.arima.estimators.hannan_rissanen import hannan_rissanen
  from statsmodels.tsa.arima.estimators.innovations import innovations, innovations_mle
  from statsmodels.tsa.arima.estimators.statespace import statespace
label_desc: Use image node_id 11 for calling a global function with example usage: test_clone() without return types