code: def segmentation_mask(N, step, width):
      mask = np.tile(np.arange(width), (N, 1))
      steps = np.array(
          [np.arange(start=0, stop=N * step, step=step)]
      ).transpose()
      steps = np.tile(steps, (1, width))
      mask = mask + steps
      return mask.flatten()
  
file_path: seglearn/seglearn/util.py
imports: from pandas import DataFrame
  import numpy
  import warnings
  from seglearn.base import TS_Data
label_desc: Use image node_id 7 for calling a global function with example usage: segmentation_mask(N, step, width) and returns: mask