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)
docstr: Rolling predict with time series pipeline.
  
  :param data: data can be a TSDataset or data creator(will be supported).
         The TSDataset should follow the same operations as the training
         TSDataset used in AutoTSEstimator.fit.
  :param batch_size: predict batch_size, the process will cost more time
         if batch_size is small while cost less memory.  The param is only
         effective when data is a TSDataset. The values defaults to 32.
docstr_tok: ["Rolling","predict","with","time","series","pipeline",".",":","param","data",":","data","can","be","a","TSDataset","or","data","creator","(","will","be","supported",")",".","The","TSDataset","should","follow","the","same","operations","as","the","training","TSDataset","used","in","AutoTSEstimator.fit",".",":","param","batch_size",":","predict","batch_size",",","the","process","will","cost","more","time","if","batch_size","is","small","while","cost","less","memory",".","The","param","is","only","effective","when","data","is","a","TSDataset",".","The","values","defaults","to","32","."]
code: def predict(self, data, batch_size=32):
      """
      Rolling predict with time series pipeline.
  
      :param data: data can be a TSDataset or data creator(will be supported).
             The TSDataset should follow the same operations as the training
             TSDataset used in AutoTSEstimator.fit.
      :param batch_size: predict batch_size, the process will cost more time
             if batch_size is small while cost less memory.  The param is only
             effective when data is a TSDataset. The values defaults to 32.
      """
      x, _ = self._tsdataset_to_numpy(data, is_predict=True)
      yhat = self._best_model.predict(x, batch_size=batch_size)
      yhat = self._tsdataset_unscale(yhat)
      return yhat
  
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 4 for calling the TSPipeline obj's underlying member method code with example usage: obj.predict(data, batch_size) and returns: yhat