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 _check_mixed_data_type_usage(self):
      for key in (
          "past_seq_len",
          "future_seq_len",
          "selected_features",
      ):
          if key not in self._best_config:
              invalidInputError(
                  False,
                  "You use a data creator to fit your AutoTSEstimator, "
                  "and use a TSDataset to predict/evaluate/fit on the TSPipeline."
                  "Please stick to the same data type.",
              )
  
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 12 for calling the TSPipeline obj's underlying member method code with example usage: obj._check_mixed_data_type_usage() without return types