code: def __init__(
      self,
      outputs,
      column_names=None,
      column_types=None,
      lookback=None,
      predict_from=1,
      predict_until=None,
      **kwargs,
  ):
      inputs = input_module.TimeseriesInput(
          lookback=lookback,
          column_names=column_names,
          column_types=column_types,
      )
      super().__init__(inputs=inputs, outputs=outputs, **kwargs)
      self.predict_from = predict_from
      self.predict_until = predict_until
      self._target_col_name = None
      self.train_len = 0
  
imports: from pathlib import Path
  from typing import Dict
  from typing import List
  from typing import Optional
  from typing import Type
  from typing import Union
  import pandas
  from autokeras import blocks
  from autokeras import nodes
  from autokeras.engine import tuner
  from autokeras.tasks import structured_data
  from autokeras.tuners import greedy
  from autokeras.utils import types
label_desc: Use image node_id 1 to create a new SupervisedTimeseriesDataPipeline object from inherited base classes: structured_data with example: obj = SupervisedTimeseriesDataPipeline(outputs, column_names, column_types, lookback, predict_from, predict_until)