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, best_model, best_config, **kwargs):
      self._best_model = best_model
      self._best_config = best_config
      self._scaler = None
      self._scaler_index = None
      if "scaler" in kwargs.keys():
          self._scaler = kwargs["scaler"]
          self._scaler_index = kwargs["scaler_index"]
  
file_path: analytics-zoo/pyzoo/zoo/chronos/autots/tspipeline.py
imports: import os
  from zoo.chronos.data import TSDataset
  from zoo.orca.automl.metrics import Evaluator
label_desc: Use image node_id 1 to create a new TSPipeline object  with example: obj = TSPipeline(best_model, best_config)