code: def sequential_window_dataset(series, window_size):
      series = tf.expand_dims(series, axis=-1)
      ds = ndarray_to_dataset(series)
      ds = ds.window(
          window_size + 1, shift=window_size, drop_remainder=True
      )
      ds = ds.flat_map(lambda window: window.batch(window_size + 1))
      ds = ds.map(lambda window: (window[:-1], window[1:]))
      return ds.batch(1).prefetch(1)
  
file_path: BigDL/python/serving/example/l08c08_forecasting_with_lstm.py
imports: import numpy
  import tensorflow
  import tensorflow.keras
label_desc: Use image node_id 6 for calling a global function with example usage: sequential_window_dataset(series, window_size) and returns: ds