code: def get_tsdata(lookback=96, horizon=320):
      name = "nyc_taxi"
      tsdata_train, _, tsdata_test = get_public_dataset(
          name, val_ratio=0, test_ratio=0.4
      )
      stand_scaler = StandardScaler()
      for tsdata in [tsdata_train, tsdata_test]:
          tsdata.gen_dt_feature(
              features=["HOUR"], one_hot_features=["HOUR"]
          ).impute(mode="linear").scale(
              stand_scaler, fit=(tsdata is tsdata_train)
          ).roll(
              lookback=lookback, horizon=horizon
          )
      return tsdata_train, tsdata_test
  
imports: from bigdl.chronos.data import get_public_dataset
  from bigdl.chronos.forecaster import TCNForecaster
  from sklearn.preprocessing import StandardScaler
  from bigdl.chronos.metric import Evaluator
  import time
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: get_tsdata(lookback, horizon) and returns: tsdata_train,  tsdata_test