code: def test_tsppl_quantize_public_dataset(self):
      tsppl_tcn = TSPipeline.load(
          os.path.join(self.resource_path, "tsppl_ckpt/tcn_tsppl_ckpt")
      )
      train_tsdata = get_test_tsdataset()
      test_tsdata = get_test_tsdataset()
      train_tsdata.roll(lookback=10, horizon=2)
      test_tsdata.roll(lookback=10, horizon=2)
      # mixed data
      tsppl_tcn._best_config.update({"selected_features": []})
      tsppl_tcn.quantize(calib_data=train_tsdata, metric="smape")
      yhat = tsppl_tcn.predict(train_tsdata)
  
imports: import numpy
  from unittest import TestCase
  import pytest
  from bigdl.chronos.utils import LazyImport
  import os
  import pandas
  import numpy
  from bigdl.chronos.data import TSDataset
  from ..None import op_torch, op_inference
label_desc: Use image node_id 9 for calling the TestTSPipeline obj's underlying member method code with example usage: obj.test_tsppl_quantize_public_dataset() without return types