code: def sliding_windows(
      cls,
      corpus: TimeSeriesCorpus,
      window_size: int,
      window_size_plus: int = 0,
      window_size_minus: int = 0,
      window_shift: int = 1,
      seed: int = 42,
  ) -> WindowsDataset:
      max_window_size = window_size + window_size_plus
      min_window_size = window_size - window_size_minus
  
      windows = []
      np.random.seed(seed)
      for scope_id, series in tqdm(
          enumerate(corpus),
          desc="Building dataset",
          total=len(corpus),
      ):
          stop = len(series) - min_window_size
          index = np.arange(stop, 0, -window_shift)
          window_length = np.random.randint(
              min_window_size, max_window_size + 1, size=index.shape
          )
          window_length = np.minimum(len(series) - index, window_length)
          windows.extend(
              zip(repeat(scope_id, len(index)), index, window_length)
          )
      return cls(corpus, windows)
  
file_path: gluonts/src/gluonts/nursery/daf/tslib/dataset/windows.py
imports: from __future__ import annotations
  from typing import Optional, List, Tuple, Any
  from itertools import repeat
  from torch.utils.data import Dataset
  from tqdm import tqdm
  import numpy
  from .timeseries import TimeSeries, TimeSeriesCorpus
label_desc: Use image node_id 5 for calling the WindowsDataset obj's underlying member method code with example usage: obj.sliding_windows(cls, corpus, window_size, window_size_plus, window_size_minus, window_shift, seed) and returns: cls