code: def main(dataset: str = "M3", group: str = "Other") -> None:
      train, horizon, freq, seasonality = get_data(
          "data/", dataset, group
      )
      if dataset == "ERCOT":
          train = train[["unique_id", "ds", "y"]]
      train["ds"] = pd.to_datetime(train["ds"])
      train = train.set_index("unique_id")
      if dataset in ["Tourism", "LongHorizon"]:
          # we add a constant to avoid zero errors
          constant = np.abs(train["y"].min()) + 10
          train["y"] += constant
  
      if dataset == "ERCOT":
          # we now ercot has strong seasonality
          model = "ZZA"
      else:
          model = "ZZZ"
      models = [ETS(season_length=seasonality, model=model)]
      ETS(season_length=seasonality).forecast(
          ap.astype(np.float32), h=12
      )
  
      start = time.time()
      fcst = StatsForecast(
          df=train, models=models, freq=freq, n_jobs=os.cpu_count()
      )
      forecasts = fcst.forecast(horizon)
      end = time.time()
      print(end - start)
  
      forecasts = forecasts.reset_index()
      forecasts.columns = ["unique_id", "ds", "ets_statsforecast"]
      if dataset in ["Tourism", "LongHorizon"]:
          # remove constant from forecasts
          forecasts["ets_statsforecast"] -= constant
      forecasts.to_csv(
          f"data/statsforecast-forecasts-{dataset}-{group}.csv",
          index=False,
      )
  
      time_df = pd.DataFrame(
          {"time": [end - start], "model": ["ets_statsforecast"]}
      )
      time_df.to_csv(
          f"data/statsforecast-time-{dataset}-{group}.csv", index=False
      )
  
file_path: statsforecast/experiments/neuralprophet/src/statsforecast.py
imports: import os
  import time
  import fire
  import numpy
  import pandas
  from statsforecast import StatsForecast
  from statsforecast.models import ETS
  from statsforecast.utils import AirPassengers
  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