code: def plot_predict(
      result,
      start=None,
      end=None,
      dynamic=False,
      alpha=0.05,
      ax=None,
      **predict_kwargs,
  ):
      """
  
      Parameters
      ----------
      result : Result
          Any model result supporting ``get_prediction``.
      start : int, str, or datetime, optional
          Zero-indexed observation number at which to start forecasting,
          i.e., the first forecast is start. Can also be a date string to
          parse or a datetime type. Default is the the zeroth observation.
      end : int, str, or datetime, optional
          Zero-indexed observation number at which to end forecasting, i.e.,
          the last forecast is end. Can also be a date string to
          parse or a datetime type. However, if the dates index does not
          have a fixed frequency, end must be an integer index if you
          want out of sample prediction. Default is the last observation in
          the sample.
      dynamic : bool, int, str, or datetime, optional
          Integer offset relative to `start` at which to begin dynamic
          prediction. Can also be an absolute date string to parse or a
          datetime type (these are not interpreted as offsets).
          Prior to this observation, true endogenous values will be used for
          prediction; starting with this observation and continuing through
          the end of prediction, forecasted endogenous values will be used
          instead.
      alpha : {float, None}
          The tail probability not covered by the confidence interval. Must
          be in (0, 1). Confidence interval is constructed assuming normally
          distributed shocks. If None, figure will not show the confidence
          interval.
      ax : AxesSubplot
          matplotlib Axes instance to use
      **predict_kwargs
          Any additional keyword arguments to pass to ``result.get_prediction``.
  
      Returns
      -------
      Figure
          matplotlib Figure containing the prediction plot
      """
      from statsmodels.graphics.utils import _import_mpl, create_mpl_ax
  
      _ = _import_mpl()
      fig, ax = create_mpl_ax(ax)
      from statsmodels.tsa.base.prediction import PredictionResults
  
      # use predict so you set dates
      pred: PredictionResults = result.get_prediction(
          start=start, end=end, dynamic=dynamic, **predict_kwargs
      )
      mean = pred.predicted_mean
      if isinstance(mean, (pd.Series, pd.DataFrame)):
          x = mean.index
          mean.plot(ax=ax, label="forecast")
      else:
          x = np.arange(mean.shape[0])
          ax.plot(x, mean, label="forecast")
  
      if alpha is not None:
          label = f"{1-alpha:.0%} confidence interval"
          ci = pred.conf_int(alpha)
          conf_int = np.asarray(ci)
  
          ax.fill_between(
              x,
              conf_int[:, 0],
              conf_int[:, 1],
              color="gray",
              alpha=0.5,
              label=label,
          )
  
      ax.legend(loc="best")
  
      return fig
  
file_path: statsmodels/statsmodels/graphics/tsaplots.py
imports: from statsmodels.compat.pandas import deprecate_kwarg
  import calendar
  import numpy
  import pandas
  from statsmodels.graphics import utils
  from statsmodels.tools.validation import array_like
  from statsmodels.tsa.stattools import acf, pacf, ccf
label_desc: Use image node_id 10 for calling a global function with example usage: plot_predict(result, start, end, dynamic, alpha, ax) and returns: fig