code: def main(dataset: str = "M3", group: str = "Other") -> None:
      train, horizon, freq, seasonality = get_data(
          "data/", dataset, group
      )
      train["ds"] = pd.to_datetime(train["ds"])
      train = train.set_index("unique_id")
  
      models = [(auto_arima_nixtla, seasonality)]
  
      start = time.time()
      fcst = StatsForecast(
          train, models=models, freq=freq, n_jobs=cpu_count()
      )
      fcst.last_dates = pd.DatetimeIndex(fcst.last_dates)
      forecasts = fcst.forecast(horizon)
      end = time.time()
      print(end - start)
  
      forecasts = forecasts.reset_index()
      forecasts.columns = ["unique_id", "ds", "auto_arima_nixtla"]
      forecasts.to_csv(
          f"data/statsforecast-forecasts-{dataset}-{group}.csv",
          index=False,
      )
  
      time_df = pd.DataFrame(
          {"time": [end - start], "model": ["auto_arima_nixtla"]}
      )
      time_df.to_csv(
          f"data/statsforecast-time-{dataset}-{group}.csv", index=False
      )
  
file_path: statsforecast/experiments/arima/src/statsforecast.py
imports: import time
  from functools import partial
  from multiprocessing import cpu_count
  import fire
  import numpy
  import pandas
  from statsforecast import StatsForecast
  from statsforecast.models import auto_arima
  from src.data import get_data
label_desc: Use image node_id 1 for calling a global function with example usage: main(dataset, group) without return types