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 get_config(self):
      return {"lookback": self.lookback, "batch_size": self.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 3 for calling the SlidingWindow obj's underlying member method code with example usage: obj.get_config() and returns: dict