code: def get_data(
      directory: str, dataset: str, group: str, train: bool = True
  ):
      if dataset == "ERCOT":
          data_location = "https://raw.githubusercontent.com/ourownstory/neuralprophet-data/main/datasets/"
          df_ercot = pd.read_csv(
              data_location + "multivariate/load_ercot_regions.csv"
          )
          df_ercot_y = pd.read_csv(
              data_location + "energy/load_ercot.csv"
          )
          df_ercot["y"] = df_ercot_y["y"]
          Y_df = df_ercot.reset_index(drop=True)
          Y_df["unique_id"] = "ERCOT"
          horizon = 24
          seasonality = 24
          freq = "H"
          Y_df_test = Y_df.groupby("unique_id").tail(horizon)
          Y_df = Y_df.drop(Y_df_test.index)
          if train:
              return Y_df, horizon, freq, seasonality
  
          return Y_df_test, horizon, freq, seasonality
  
      if train:
          return Y_df, horizon, freq, seasonality
  
      if dataset not in dict_datasets.keys():
          raise Exception(f"dataset {dataset} not found")
  
      dataclass, datainfo = dict_datasets[dataset]
      if group not in datainfo.groups:
          raise Exception(f"group {group} not found for {dataset}")
  
      Y_df, *_ = dataclass.load(directory, group)
  
      if dataset == "LongHorizon":
          horizon = datainfo[group].horizons[0]
      else:
          horizon = datainfo[group].horizon
      freq = datainfo[group].freq
      if group == "ETTm2":
          seasonality = 24
      else:
          seasonality = datainfo[group].seasonality
      Y_df_test = Y_df.groupby("unique_id").tail(horizon)
      Y_df = Y_df.drop(Y_df_test.index)
  
      if train:
          return Y_df, horizon, freq, seasonality
  
      return Y_df_test, horizon, freq, seasonality
  
file_path: statsforecast/experiments/neuralprophet/src/data.py
imports: import fire
  import pandas
  from neuralforecast.data.datasets.m3 import M3, M3Info
  from neuralforecast.data.datasets.m4 import M4, M4Info
  from neuralforecast.data.datasets.tourism import Tourism, TourismInfo
  from neuralforecast.data.datasets.long_horizon import LongHorizon, LongHorizonInfo
label_desc: Use image node_id 1 for calling a global function with example usage: get_data(directory, dataset, group, train) and returns: Y_df_test,  horizon,  freq,  seasonality, Y_df_test,  horizon,  freq,  seasonality, Y_df,  horizon,  freq,  seasonality, Y_df,  horizon,  freq,  seasonality, Y_df,  horizon,  freq,  seasonality