code: def test_double_pickle():
      arima = ARIMA(order=(0, 0, 0), trend="c", suppress_warnings=True)
      arima.fit(y)
  
      with tempfile.TemporaryDirectory() as tdir:
          # Now save it twice
          file_a = os.path.join(tdir, "first.pkl")
          file_b = os.path.join(tdir, "second.pkl")
  
          # No compression
          joblib.dump(arima, file_a)
  
          # Sleep between pickling so that the "pickle hash" for the ARIMA is
          # different by enough. We could theoretically also just use a UUID
          # for part of the hash to make sure it's unique?
          time.sleep(0.5)
  
          # Some compression
          joblib.dump(arima, file_b, compress=2)
  
          # Load both and prove they can both predict
          loaded_a = joblib.load(file_a)  # type: ARIMA
          loaded_b = joblib.load(file_b)  # type: ARIMA
          pred_a = loaded_a.predict(n_periods=5)
          pred_b = loaded_b.predict(n_periods=5)
          assert np.allclose(pred_a, pred_b)
  
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 16 for calling a global function with example usage: test_double_pickle() without return types