code: def test_forecast():
      # Numpy
      mod = MLEModel([1, 2], **kwargs)
      res = mod.filter([])
      forecast = res.forecast(steps=10)
      assert_allclose(forecast, np.ones((10,)) * 2)
      assert_allclose(
          res.get_forecast(steps=10).predicted_mean, forecast
      )
  
      # Pandas
      index = pd.date_range("1960-01-01", periods=2, freq="MS")
      mod = MLEModel(pd.Series([1, 2], index=index), **kwargs)
      res = mod.filter([])
      assert_allclose(res.forecast(steps=10), np.ones((10,)) * 2)
      assert_allclose(
          res.forecast(steps="1960-12-01"), np.ones((10,)) * 2
      )
      assert_allclose(
          res.get_forecast(steps=10).predicted_mean, np.ones((10,)) * 2
      )
  
imports: from statsmodels.compat.pandas import MONTH_END
  import os
  import re
  import warnings
  import numpy
  from numpy.testing import assert_, assert_allclose, assert_almost_equal, assert_equal, assert_raises
  import pandas
  import pytest
  from statsmodels.datasets import nile
  from statsmodels.tsa.statespace import kalman_filter, kalman_smoother, sarimax, varmax
  from statsmodels.tsa.statespace.mlemodel import MLEModel, MLEResultsWrapper
  from statsmodels.tsa.statespace.tests.results import results_sarimax, results_var_misc
label_desc: Use image node_id 16 for calling a global function with example usage: test_forecast() without return types