code: def auto_arima_pmdarima(X, h, future_xreg, season_length):
      y = X[:, 0] if X.ndim == 2 else X
      xreg = X[:, 1:] if (X.ndim == 2 and X.shape[1] > 1) else None
      mod = auto_arima(
          y,
          X=xreg,
          method="bfgs",
          information_criterion="aicc",
          m=season_length,
          with_intercept=False,
          error_action="ignore",
      )
  
      return mod.predict(h, X=future_xreg)
  
file_path: statsforecast/experiments/arima_xreg/src/pmdarima.py
imports: import time
  from functools import partial
  from multiprocessing import cpu_count
  import fire
  import numpy
  import pandas
  from pmdarima.arima import auto_arima
  from statsforecast import StatsForecast
  from src.data import get_data
label_desc: Use image node_id 1 for calling a global function with example usage: auto_arima_pmdarima(X, h, future_xreg, season_length) and returns: mod