code: def _mask(self, mask, max_mask_patches):
      delta = 0
      for _ in range(10):
          target_area = random.uniform(
              self.min_num_patches, max_mask_patches
          )
          aspect_ratio = math.exp(
              random.uniform(*self.log_aspect_ratio)
          )
          h = int(round(math.sqrt(target_area * aspect_ratio)))
          w = int(round(math.sqrt(target_area / aspect_ratio)))
          if w < self.width and h < self.height:
              top = random.randint(0, self.height - h)
              left = random.randint(0, self.width - w)
  
              num_masked = mask[top : top + h, left : left + w].sum()
              # Overlap
              if 0 < h * w - num_masked <= max_mask_patches:
                  for i in range(top, top + h):
                      for j in range(left, left + w):
                          if mask[i, j] == 0:
                              mask[i, j] = 1
                              delta += 1
  
              if delta > 0:
                  break
      return delta
  
imports: import logging
  import math
  import numpy
  import random
  import torch
  import torchvision
  import torchvision.transforms.functional
  from PIL import Image, ImageFilter
  from scipy.ndimage import gaussian_filter
  from torchvision import transforms
  from .rand_augment import rand_augment_transform
  from .random_erasing import RandomErasing
label_desc: Use image node_id 4 for calling the MaskingGenerator obj's underlying member method code with example usage: obj._mask(mask, max_mask_patches) and returns: delta