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_unscale(self, y):
      if self._scaler:
          from bigdl.chronos.data.utils.scale import (
              unscale_timeseries_numpy,
          )
  
          y = unscale_timeseries_numpy(
              y, self._scaler, self._scaler_index
          )
      return y
  
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 13 for calling the TSPipeline obj's underlying member method code with example usage: obj._tsdataset_unscale(y) and returns: y