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 _tsdataset_to_numpy(self, data, is_predict=False):
      if isinstance(data, TSDataset):
          lookback = self._best_config["past_seq_len"]
          horizon = (
              0 if is_predict else self._best_config["future_seq_len"]
          )
          selected_features = self._best_config["selected_features"]
          data.roll(lookback, horizon, feature_col=selected_features)
          x, y = data.to_numpy()
      else:
          raise NotImplementedError(
              "Data creator has not been supported now."
          )
      return x, y
  
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 9 for calling the TSPipeline obj's underlying member method code with example usage: obj._tsdataset_to_numpy(data, is_predict) and returns: x,  y