code: def test_predict_plot(use_pandas, model_and_args, alpha):
      model, kwargs = model_and_args
      rs = np.random.RandomState(0)
      y = rs.standard_normal(1000)
      for i in range(2, 1000):
          y[i] += 1.8 * y[i - 1] - 0.9 * y[i - 2]
      y = y[100:]
      if use_pandas:
          index = pd.date_range(
              "1960-1-1", freq=MONTH_END, periods=y.shape[0] + 24
          )
          start = index[index.shape[0] // 2]
          end = index[-1]
          y = pd.Series(y, index=index[:-24])
      else:
          start = y.shape[0] // 2
          end = y.shape[0] + 24
      res = model(y, **kwargs).fit()
      fig = plot_predict(res, start, end, alpha=alpha)
      assert isinstance(fig, plt.Figure)
  
imports: from statsmodels.compat.pandas import MONTH_END
  from statsmodels.compat.python import lmap
  import calendar
  from io import BytesIO
  import locale
  import numpy
  from numpy.testing import assert_, assert_equal
  import pandas
  import pytest
  from statsmodels.datasets import elnino, macrodata
  from statsmodels.graphics.tsaplots import month_plot, plot_accf_grid, plot_acf, plot_ccf, plot_pacf, plot_predict, quarter_plot, seasonal_plot
  from statsmodels.tsa import arima_process
  from statsmodels.tsa.ar_model import AutoReg
  from statsmodels.tsa.arima.model import ARIMA
label_desc: Use image node_id 13 for calling a global function with example usage: test_predict_plot(use_pandas, model_and_args, alpha) without return types