code: def test_with_seasonality():
      fit = ARIMA(
          order=(1, 1, 1),
          seasonal_order=(0, 1, 1, 12),
          suppress_warnings=True,
      ).fit(y=wineind)
      _try_get_attrs(fit)
  
      # R code AIC result is ~3004
      assert abs(fit.aic() - 3004) < 100  # show equal within 100 or so
  
      # R code AICc result is ~3005
      assert abs(fit.aicc() - 3005) < 100  # show equal within 100 or so
  
      # R code BIC result is ~3017
      assert abs(fit.bic() - 3017) < 100  # show equal within 100 or so
  
      # show we can predict in-sample
      fit.predict_in_sample()
  
      # test with SARIMAX confidence intervals
      fit.predict(n_periods=10, return_conf_int=True, alpha=0.05)
  
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 15 for calling a global function with example usage: test_with_seasonality() without return types