class_docstr: TSPipeline is an E2E solution for time series analysis (only forecasting task for now).
  You can use TSPipeline to:
  
  1. Further development on the prototype. (predict, evaluate, incremental fit)
  
  2. Deploy the model to their scenario. (save, load)
code: def __init__(
      self,
      model,
      loss,
      optimizer,
      model_creator,
      loss_creator,
      optimizer_creator,
      best_config,
      **kwargs,
  ):
      from bigdl.nano.pytorch.trainer import Trainer
  
      # for runtime fit/predict/evaluate
      self._best_model = Trainer.compile(
          model=model, loss=loss, optimizer=optimizer
      )
      self._best_config = best_config
      self._onnxruntime_fp32 = None
      self._onnxruntime_int8 = None
      self._pytorch_int8 = None
  
      # for data postprocessing
      self._scaler = None
      self._scaler_index = None
      if "scaler" in kwargs.keys():
          self._scaler = kwargs["scaler"]
          self._scaler_index = kwargs["scaler_index"]
  
      # for save/load
      self.model_creator = model_creator
      self.loss_creator = loss_creator
      self.optimizer_creator = optimizer_creator
  
imports: import os
  from bigdl.chronos.utils import LazyImport
  import types
  import numpy
  from bigdl.chronos.data import TSDataset
  from bigdl.chronos.metric.forecast_metrics import Evaluator
  from bigdl.chronos.pytorch.utils import _pytorch_fashion_inference
  from bigdl.nano.utils.common import invalidInputError
label_desc: Use image node_id 1 to create a new TSPipeline object  with example: obj = TSPipeline(model, loss, optimizer, model_creator, loss_creator, optimizer_creator, best_config)