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_loader(self, data, is_predict=False, batch_size=32):
      self._check_mixed_data_type_usage()
      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_loader = data.to_torch_data_loader(
          batch_size=batch_size,
          lookback=lookback,
          horizon=horizon,
          feature_col=selected_features,
      )
      return data_loader
  
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 10 for calling the TSPipeline obj's underlying member method code with example usage: obj._tsdataset_to_loader(data, is_predict, batch_size) and returns: data_loader