code: def test_s2s_forecaster_export_forecasting_pipeline(self):
      import shutil
      import pandas as pd
      from sklearn.preprocessing import StandardScaler
      from numpy.testing import assert_array_almost_equal
      from bigdl.chronos.data import TSDataset
  
      temp_dir = tempfile.mkdtemp()
  
      # developing
      train_data, test_data = create_tsdataset(roll=False)
      scaler = StandardScaler()
  
      train_data.scale(scaler, fit=True).roll(lookback=24, horizon=5)
  
      forecaster = Seq2SeqForecaster.from_tsdataset(train_data)
      forecaster.fit(train_data)
  
      # export the pipeline to torchscript
      pipeline_module_dir = os.path.join(temp_dir, "pipeline")
      os.mkdir(pipeline_module_dir)
      forecaster.export_torchscript_file(
          dirname=pipeline_module_dir,
          save_pipeline=True,
          tsdata=train_data,
          drop_dt_col=True,
      )
      # save the test data for deployment
      test_data_path = os.path.join(temp_dir, "inference_data.csv")
      test_data.df.to_csv(test_data_path, index=False)
  
      # deployment
      test_df = pd.read_csv(test_data_path, parse_dates=["timeseries"])
      test_data = TSDataset.from_pandas(
          df=test_df,
          dt_col="timeseries",
          target_col=["value1", "value2"],
          deploy_mode=True,
      )
      test_data.df.drop(columns=test_data.dt_col, inplace=True)
      test_data.df["id"] = np.array([0] * len(test_data.df))
      input_tensor = torch.from_numpy(test_data.df.values)
      pipeline_module_path = os.path.join(
          pipeline_module_dir, "chronos_forecasting_pipeline.pt"
      )
      pipeline_module = torch.jit.load(pipeline_module_path)
      output = pipeline_module.forward(input_tensor)
  
      # compare the result of forecaster.predict_with_jit()
      test_data.scale(scaler).roll(lookback=24, horizon=5)
      input_data = test_data.to_numpy()
      forecaster_output = forecaster.predict_with_jit(input_data)
      postprocess_output = test_data.unscale_numpy(forecaster_output)
      assert_array_almost_equal(output.numpy(), postprocess_output)
  
      if os.path.exists(temp_dir):
          shutil.rmtree(temp_dir)
  
imports: import numpy
  import tempfile
  import os
  from bigdl.chronos.utils import LazyImport
  from unittest import TestCase
  import pytest
  from ..None import op_torch, op_distributed, op_inference, op_automl
label_desc: Use image node_id 30 for calling the TestChronosModelSeq2SeqForecaster obj's underlying member method code with example usage: obj.test_s2s_forecaster_export_forecasting_pipeline() without return types