code: def gen_dataloader():
      tsdata_train, tsdata_val, tsdata_test = get_public_dataset(
          name="nyc_taxi",
          with_split=True,
          val_ratio=0.1,
          test_ratio=0.1,
      )
  
      stand = StandardScaler()
      for tsdata in [tsdata_train, tsdata_val, tsdata_test]:
          tsdata.deduplicate().impute().gen_dt_feature().scale(
              stand, fit=tsdata is tsdata_train
          ).roll(lookback=48, horizon=1)
  
      tsdata_traindataloader = tsdata_train.to_torch_data_loader(
          batch_size=32, roll=False
      )
      tsdata_valdataloader = tsdata_val.to_torch_data_loader(
          batch_size=32, roll=False, shuffle=False
      )
      tsdata_testdataloader = tsdata_test.to_torch_data_loader(
          batch_size=32, roll=False, shuffle=False
      )
  
      return (
          tsdata_traindataloader,
          tsdata_valdataloader,
          tsdata_testdataloader,
      )
  
file_path: BigDL/python/chronos/example/inference-acceleration/cpu_inference_acceleration.py
imports: import torch
  from bigdl.chronos.pytorch import TSTrainer
  from bigdl.chronos.model.tcn import model_creator
  from bigdl.chronos.metric import Evaluator
  from bigdl.chronos.data import get_public_dataset
  from sklearn.preprocessing import StandardScaler
  from bigdl.chronos.pytorch import TSInferenceOptimizer
label_desc: Use image node_id 1 for calling a global function with example usage: gen_dataloader() and returns: tsdata_traindataloader,  tsdata_valdataloader,  tsdata_testdataloader