code: def print_statsforecast_ARIMA(model, digits=3, se=True):
      print(arima_string(model, padding=False))
      if model["lambda"] is not None:
          print(f'Box Cox transformation: lambda={model["lambda"]}')
      if len(model["coef"]) > 0:
          print("\nCoefficients:")
          coef = [
              round(coef, ndigits=digits)
              for coef in model["coef"].values()
          ]
          if se and len(model["var_coef"]):
              ses = np.zeros(len(coef))
              ses[model["mask"]] = np.sqrt(
                  np.diag(model["var_coef"])
              ).round(decimals=digits)
              coef = pd.DataFrame(
                  np.stack([coef, ses]),
                  columns=model["coef"].keys(),
                  index=["coefficient", "s.e."],
              )
              if "intercept" in coef.columns:
                  coef = coef.rename(columns={"intercept": "mean"})
              print(coef)
      else:
          print(
              "This model does not have coefficients, be sure its properly trained."
          )
      print(
          f'\nsigma^2 = {round(model["sigma2"], ndigits=digits)}',
          end="",
      )
      if model["loglik"] is not None:
          print(f': log likelihood = {model["loglik"]:.2f}', end="")
      print("\n")
      if not np.isnan(model["aic"]):
          print(f'AIC={round(model["aic"], 2)}')
  
imports: import math
  import warnings
  from collections import namedtuple
  from functools import partial
  from typing import Optional, Dict, Union, Tuple
  import numpy
  import pandas
  import statsmodels.api
  from numba import njit
  from scipy.optimize import minimize
  from scipy.stats import norm
  from .mstl import mstl
  from .utils import CACHE, NOGIL
label_desc: Use image node_id 38 for calling a global function with example usage: print_statsforecast_ARIMA(model, digits, se) without return types