code: def plot_var_forc(
      prior,
      forc,
      err_upper,
      err_lower,
      index=None,
      names=None,
      plot_stderr=True,
      legend_options=None,
  ):
      import matplotlib.pyplot as plt
  
      n, k = prior.shape
      rows, cols = k, 1
  
      fig = plt.figure(figsize=(10, 10))
  
      prange = np.arange(n)
      rng_f = np.arange(n - 1, n + len(forc))
      rng_err = np.arange(n, n + len(forc))
  
      for j in range(k):
          ax = plt.subplot(rows, cols, j + 1)
  
          p1 = ax.plot(prange, prior[:, j], "k", label="Observed")
          p2 = ax.plot(
              rng_f,
              np.r_[prior[-1:, j], forc[:, j]],
              "k--",
              label="Forecast",
          )
  
          if plot_stderr:
              p3 = ax.plot(
                  rng_err,
                  err_upper[:, j],
                  "k-.",
                  label="Forc 2 STD err",
              )
              ax.plot(rng_err, err_lower[:, j], "k-.")
  
          if names is not None:
              ax.set_title(names[j])
  
          if legend_options is None:
              legend_options = {"loc": "upper right"}
          ax.legend(**legend_options)
      return fig
  
file_path: statsmodels/statsmodels/tsa/vector_ar/plotting.py
imports: from statsmodels.compat.python import lrange
  import numpy
  import statsmodels.tsa.vector_ar.util
label_desc: Use image node_id 2 for calling a global function with example usage: plot_var_forc(prior, forc, err_upper, err_lower, index, names, plot_stderr, legend_options) and returns: fig