code: def test_update_1_iter(model):
      # The model should *barely* change if we update with one iter.
      endog = wineind
      train, test = endog[:145], endog[145:]
  
      model.fit(train)
      params1 = model.params()
  
      # Now update with 1 iteration, and show params have not changed too much
      model.update(test, maxiter=1)
      params2 = model.params()
  
      # They should be close
      assert np.allclose(params1, params2, atol=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 25 for calling a global function with example usage: test_update_1_iter(model) without return types