code: def test_more_elaborate():
      # show we can fit this with a non-zero order
      arima = ARIMA(order=(2, 1, 2), suppress_warnings=True).fit(y=hr)
      _try_get_attrs(arima)
  
      # can we fit this same arima with a made-up X array?
      xreg = rs.rand(hr.shape[0], 4)
      arima = ARIMA(order=(2, 1, 2), suppress_warnings=True).fit(
          y=hr, X=xreg
      )
      _try_get_attrs(arima)
  
      with tempfile.TemporaryDirectory() as tdir:
          # pickle this for the __get/setattr__ coverage.
          # since the only time this is tested is in parallel in auto.py,
          # this doesn't actually get any coverage proof...
          fl = os.path.join(tdir, "some_temp_file.pkl")
          with open(fl, "wb") as p:
              pickle.dump(arima, p)
  
          # show we can predict with this even though it's been pickled
          new_xreg = rs.rand(5, 4)
          _preds = arima.predict(n_periods=5, X=new_xreg)
  
          # now unpickle
          with open(fl, "rb") as p:
              other = pickle.load(p)
  
          # show we can still predict, compare
          _other_preds = other.predict(n_periods=5, X=new_xreg)
          assert_array_almost_equal(_preds, _other_preds)
  
      # now show that since we fit the ARIMA with an X array,
      # we need to provide one for predictions otherwise it breaks.
      with pytest.raises(ValueError):
          arima.predict(n_periods=5, X=None)
  
      # show that if we DO provide an X and it's the wrong dims, we
      # also break things down.
      with pytest.raises(ValueError):
          arima.predict(n_periods=5, X=rs.rand(4, 4))
  
imports: import numpy
  import pandas
  from pmdarima.arima import ARIMA, auto_arima, AutoARIMA, ARMAtoMA
  from pmdarima.arima import _validation
  from pmdarima.compat.pytest import pytest_error_str
  from pmdarima.datasets import load_lynx, load_wineind, load_heartrate
  from numpy.random import RandomState
  from numpy.testing import assert_array_almost_equal, assert_almost_equal, assert_allclose
  from statsmodels import api
  from sklearn.metrics import mean_squared_error
  import datetime
  import joblib
  import os
  import pickle
  import pytest
  import tempfile
  import time
label_desc: Use image node_id 13 for calling a global function with example usage: test_more_elaborate() without return types