code: def get_tsdata():
      name = "nyc_taxi"
      tsdata_train, tsdata_valid, tsdata_test = get_public_dataset(name)
      stand_scaler = StandardScaler()
      for tsdata in [tsdata_train, tsdata_valid, tsdata_test]:
          tsdata.impute(mode="linear").scale(
              stand_scaler, fit=(tsdata is tsdata_train)
          )
      return tsdata_train, tsdata_valid, tsdata_test
  
file_path: BigDL/python/chronos/example/auto_model/autolstm_nyc_taxi.py
imports: import torch
  import numpy
  import argparse
  from sklearn.preprocessing import StandardScaler
  from bigdl.chronos.data import get_public_dataset
  from bigdl.chronos.autots.model.auto_lstm import AutoLSTM
  from bigdl.orca.automl import hp
  from bigdl.orca import init_orca_context, stop_orca_context
  from bigdl.orca.automl.metrics import Evaluator
label_desc: Use image node_id 1 for calling a global function with example usage: get_tsdata() and returns: tsdata_train,  tsdata_valid,  tsdata_test