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 = [AutoCES(season_length=seasonality)]
      levels = [k for k in range(55, 100, 5)]
      start = time.time()
      fcst = StatsForecast(
          df=train,
          models=models,
          freq=freq,
          n_jobs=cpu_count(),
          fallback_model=Naive(),
      )
      forecasts = fcst.forecast(h=horizon, level=levels)
      end = time.time()
  
      forecasts = forecasts.reset_index()
      forecasts.columns = (
          ["unique_id", "ds", "statsforecast_mean"]
          + ["statsforecast_lowerb_" + str(i) for i in reversed(levels)]
          + ["statsforecast_upperb_" + str(i) for i in levels]
      )
      forecasts = forecasts[
          ["unique_id", "ds", "statsforecast_mean"]
          + ["statsforecast_lowerb_" + str(i) for i in levels]
          + ["statsforecast_upperb_" + str(i) for i in levels]
      ]
      forecasts.to_csv(
          f"data/statsforecast-ces-forecasts-{dataset}-{group}-pred-int.csv",
          index=False,
      )
  
      time_df = pd.DataFrame(
          {"time": [end - start], "model": ["ces_statsforecast"]}
      )
      time_df.to_csv(
          f"data/statsforecast-ces-time-{dataset}-{group}-pred-int.csv",
          index=False,
      )
  
file_path: statsforecast/experiments/ces_intervals/src/nb_statsforecast.py
imports: import os
  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 AutoCES, Naive
  from statsforecast.utils import AirPassengers
  from src.data import get_data
  from datasetsforecast.m4 import M4
label_desc: Use image node_id 1 for calling a global function with example usage: main(dataset, group) without return types