code: def rle_to_mask(rle: Dict[str, Any]) -> np.ndarray:
      """Compute a binary mask from an uncompressed RLE."""
      h, w = rle["size"]
      mask = np.empty(h * w, dtype=bool)
      idx = 0
      parity = False
      for count in rle["counts"]:
          mask[idx : idx + count] = parity
          idx += count
          parity ^= True
      mask = mask.reshape(w, h)
      return mask.transpose()
  
imports: import numpy
  import paddle
  import math
  from copy import deepcopy
  from itertools import product
  from typing import Any, Dict, Generator, ItemsView, List, Tuple
label_desc: Use image node_id 5 for calling a global function with example usage: rle_to_mask(rle) and returns: mask