code: def generate_data():
      tsdata_train, tsdata_val, tsdata_test = get_public_dataset(
          name="tsinghua_electricity",
          with_split=True,
          val_ratio=0.1,
          test_ratio=0.2,
      )
      standard_scaler = StandardScaler()
      for tsdata in [tsdata_train, tsdata_test]:
          tsdata.impute(mode="last").scale(
              standard_scaler, fit=(tsdata is tsdata_train)
          )
  
      train_loader = tsdata_train.to_torch_data_loader(
          lookback=look_back, horizon=horizon
      )
      test_loader = tsdata_test.to_torch_data_loader(
          batch_size=1,
          lookback=look_back,
          horizon=horizon,
          shuffle=False,
      )
  
      return train_loader, test_loader
  
file_path: BigDL/python/chronos/use-case/electricity/tcn.py
imports: from bigdl.chronos.forecaster import TCNForecaster
  import pandas
  from bigdl.chronos.data import TSDataset, get_public_dataset
  from sklearn.preprocessing import StandardScaler
  from bigdl.chronos.metric.forecast_metrics import Evaluator
  import torch
  import time
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: generate_data() and returns: train_loader,  test_loader