code: def create_data():
      tsdata_train, tsdata_val, tsdata_test = get_public_dataset(
          name="nyc_taxi"
      )
  
      scaler = StandardScaler()
  
      for tsdata in [tsdata_train, tsdata_val, tsdata_test]:
          tsdata.deduplicate().impute().gen_dt_feature().scale(
              scaler, fit=(tsdata is tsdata_train)
          ).roll(lookback=lookback, horizon=horizon)
  
      return tsdata_train, tsdata_val, tsdata_test
  
file_path: BigDL/python/chronos/benchmark/forecaster_inference/forecaster_inference.py
imports: import time
  import json
  import argparse
  from bigdl.chronos.forecaster import TCNForecaster
  from bigdl.chronos.data import get_public_dataset
  from sklearn.preprocessing import StandardScaler
label_desc: Use image node_id 1 for calling a global function with example usage: create_data() and returns: tsdata_train,  tsdata_val,  tsdata_test