code: def split_combined_polys(polys, poly_lens, polys_per_mask):
      """Split the combined 1-D polys into masks.
  
      A mask is represented as a list of polys, and a poly is represented as
      a 1-D array. In dataset, all masks are concatenated into a single 1-D
      tensor. Here we need to split the tensor into original representations.
  
      Args:
          polys (list): a list (length = image num) of 1-D tensors
          poly_lens (list): a list (length = image num) of poly length
          polys_per_mask (list): a list (length = image num) of poly number
              of each mask
  
      Returns:
          list: a list (length = image num) of list (length = mask num) of \
              list (length = poly num) of numpy array.
      """
      mask_polys_list = []
      for img_id in range(len(polys)):
          polys_single = polys[img_id]
          polys_lens_single = poly_lens[img_id].tolist()
          polys_per_mask_single = polys_per_mask[img_id].tolist()
  
          split_polys = slice_list(polys_single, polys_lens_single)
          mask_polys = slice_list(split_polys, polys_per_mask_single)
          mask_polys_list.append(mask_polys)
      return mask_polys_list
  
imports: import numpy
  import pycocotools.mask
  import torch
  from mmengine.utils import slice_list
label_desc: Use image node_id 1 for calling a global function with example usage: split_combined_polys(polys, poly_lens, polys_per_mask) and returns: mask_polys_list