code: def __call__(self, img, iter_i=None, raw_image=None):
      self.img_h, self.img_w, c = img.shape
      mask = np.zeros((self.img_h, self.img_w), np.float32)
      at_least_one_mask_applied = False
  
      if self.is_fixed_randomness:
          assert (
              raw_image is not None
          ), f"Cant calculate hash on raw_image=None"
          rs = self._img2rs(raw_image)
          self.rnd = np.random.RandomState(rs)
      else:
          self.rnd = np.random
  
      coords = [
          [(0, 0), (1, self.get_padding(size=self.img_h))],
          [(0, 0), (self.get_padding(size=self.img_w), 1)],
          [(0, 1 - self.get_padding(size=self.img_h)), (1, 1)],
          [(1 - self.get_padding(size=self.img_w), 0), (1, 1)],
      ]
  
      for pp, coord in zip(self.probs, coords):
          if self.rnd.random() < pp:
              at_least_one_mask_applied = True
              mask = self.apply_padding(mask=mask, coord=coord)
  
      if not at_least_one_mask_applied:
          idx = self.rnd.choice(
              range(len(coords)),
              p=np.array(self.probs) / sum(self.probs),
          )
          mask = self.apply_padding(mask=mask, coord=coords[idx])
      return mask[..., None]
  
imports: import math
  import random
  import hashlib
  import logging
  from enum import Enum
  import cv2
  import numpy
label_desc: Use image node_id 5 for calling the OutpaintingMaskGenerator obj's underlying member method code with example usage: obj.__call__(img, iter_i, raw_image) and returns: mask