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.
         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.
  :param quantize: if use the quantized model to predict.
docstr_tok: ["Rolling","predict","with","time","series","pipeline",".",":","param","data",":","data","can","be","a","TSDataset","or","data","creator",".","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.",":","param","quantize",":","if","use","the","quantized","model","to","predict","."]
code: def predict(self, data, batch_size=32, quantize=False):
      """
      Rolling predict with time series pipeline.
  
      :param data: data can be a TSDataset or data creator.
             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.
      :param quantize: if use the quantized model to predict.
      """
      if isinstance(data, TSDataset):
          x = self._tsdataset_to_numpy(data, is_predict=True)
          if quantize:
              with torch.inference_mode():
                  yhat = _pytorch_fashion_inference(
                      model=self._pytorch_int8,
                      input_data=x,
                      batch_size=batch_size,
                  )
          else:
              self._best_model.eval()
              with torch.inference_mode():
                  yhat = _pytorch_fashion_inference(
                      model=self._best_model,
                      input_data=x,
                      batch_size=batch_size,
                  )
          yhat = self._tsdataset_unscale(yhat)
      elif isinstance(data, types.FunctionType):
          yhat_list = []
          self._best_config.update({"batch_size": batch_size})
          for x, _ in data(self._best_config):
              if quantize:
                  with torch.inference_mode():
                      yhat = _pytorch_fashion_inference(
                          model=self._pytorch_int8, input_data=x.numpy()
                      )
              else:
                  self._best_model.eval()
                  with torch.inference_mode():
                      yhat = _pytorch_fashion_inference(
                          model=self._best_model, input_data=x.numpy()
                      )
              yhat_list.append(yhat)
          yhat = np.concatenate(yhat_list, axis=0)
      else:
          invalidInputError(
              False,
              "We only support input tsdataset or data creator, "
              f"but found {data.__class__.__name__}",
          )
      return yhat
  
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 4 for calling the TSPipeline obj's underlying member method code with example usage: obj.predict(data, batch_size, quantize) and returns: yhat