code: def test_predict_ranges(austourists_model_fit):
      # in total 68 observations
      fit = austourists_model_fit
  
      # first prediction is 0, last is 10 -> 11 predictions
      pred = fit.predict(start=0, end=10)
      assert len(pred) == 11
  
      pred = fit.predict(start=10, end=20)
      assert len(pred) == 11
  
      pred = fit.predict(start=10, dynamic=10, end=30)
      assert len(pred) == 21
  
      # try boolean dynamic
      pred = fit.predict(start=0, dynamic=True, end=70)
      assert len(pred) == 71
      pred = fit.predict(start=0, dynamic=True, end=70)
      assert len(pred) == 71
  
      # try only out oof sample prediction
      pred = fit.predict(start=80, end=84)
      assert len(pred) == 5
  
imports: from statsmodels.compat.pandas import QUARTER_END
  from statsmodels.compat.platform import PLATFORM_LINUX32, PLATFORM_WIN
  from itertools import product
  import json
  import pathlib
  import numpy
  from numpy.testing import assert_allclose, assert_almost_equal
  import pandas
  import pytest
  import scipy.stats
  from statsmodels.tsa.exponential_smoothing.ets import ETSModel
  import statsmodels.tsa.holtwinters
  import statsmodels.tsa.statespace.exponential_smoothing
label_desc: Use image node_id 28 for calling a global function with example usage: test_predict_ranges(austourists_model_fit) without return types