code: def get_tsdata():
      name = "network_traffic"
      tsdata_train, _, tsdata_test = get_public_dataset(
          name, val_ratio=0
      )
      minmax = MinMaxScaler()
      for tsdata in [tsdata_train, tsdata_test]:
          tsdata.gen_dt_feature(
              one_hot_features=["HOUR", "WEEK"]
          ).impute("last").scale(
              minmax, fit=tsdata is tsdata_train
          ).roll(
              lookback=100, horizon=10
          )
      return tsdata_train, tsdata_test
  
file_path: BigDL/python/chronos/example/distributed/distributed_training_network_traffic.py
imports: import argparse
  import numpy
  from sklearn.preprocessing import MinMaxScaler
  from bigdl.orca.automl.metrics import Evaluator
  from bigdl.orca import init_orca_context, stop_orca_context
  from bigdl.chronos.forecaster.seq2seq_forecaster import Seq2SeqForecaster
  from bigdl.chronos.data import get_public_dataset
label_desc: Use image node_id 1 for calling a global function with example usage: get_tsdata() and returns: tsdata_train,  tsdata_test