class_docstr: Apply sliding window to the dataset.
  
  It groups the consecutive data items together. Therefore, it inserts one
  more dimension of size `lookback` to the dataset shape after the batch_size
  dimension. It also reduce the number of instances in the dataset by
  (lookback - 1).
  
  # Arguments
      lookback: Int. The window size. The number of data items to group
          together.
      batch_size: Int. The batch size of the dataset.
code: def __init__(self, lookback, batch_size, **kwargs):
      super().__init__(**kwargs)
      self.lookback = lookback
      self.batch_size = batch_size
  
imports: import tensorflow
  from autokeras import analysers
  from autokeras import keras_layers
  from autokeras import preprocessors
  from autokeras.engine import preprocessor
  from autokeras.utils import data_utils
label_desc: Use image node_id 1 to create a new SlidingWindow object from inherited base classes: preprocessor with example: obj = SlidingWindow(lookback, batch_size)