code: def test_predict():
      dates = pd.date_range(
          start="1980-01-01", end="1981-01-01", freq="YS"
      )
      endog = pd.Series([1, 2], index=dates)
      mod = MLEModel(endog, **kwargs)
      res = mod.filter([])
  
      # Test that predict with start=None, end=None does prediction with full
      # dataset
      predict = res.predict()
      assert_equal(predict.shape, (mod.nobs,))
      assert_allclose(res.get_prediction().predicted_mean, predict)
  
      # Test a string value to the dynamic option
      assert_allclose(res.predict(dynamic="1981-01-01"), res.predict())
  
      # Test an invalid date string value to the dynamic option
      # assert_raises(ValueError, res.predict, dynamic='1982-01-01')
  
      # Test for passing a string to predict when dates are not set
      mod = MLEModel([1, 2], **kwargs)
      res = mod.filter([])
      assert_raises(KeyError, res.predict, dynamic="string")
  
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 15 for calling a global function with example usage: test_predict() without return types