code: def _mask(self, mask, max_mask_patches):
      delta = 0
      for _attempt in range(10):
          target_area = random.uniform(
              self.mask_group_min_patches, max_mask_patches
          )
          aspect_ratio = math.exp(
              random.uniform(*self.log_aspect_ratio)
          )
          height = int(round(math.sqrt(target_area * aspect_ratio)))
          width = int(round(math.sqrt(target_area / aspect_ratio)))
          if width < self.width and height < self.height:
              top = random.randint(0, self.height - height)
              left = random.randint(0, self.width - width)
  
              num_masked = mask[
                  top : top + height, left : left + width
              ].sum()
              # Overlap
              if 0 < height * width - num_masked <= max_mask_patches:
                  for i in range(top, top + height):
                      for j in range(left, left + width):
                          if mask[i, j] == 0:
                              mask[i, j] = 1
                              delta += 1
  
              if delta > 0:
                  break
      return delta
  
imports: import math
  import random
  from functools import lru_cache
  from typing import Any, List, Optional, Tuple, Union
  import numpy
  from PIL import Image
  from transformers.image_utils import PILImageResampling
  from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
  from ...image_utils import ImageFeatureExtractionMixin, is_torch_tensor
  from ...utils import TensorType, logging
label_desc: Use image node_id 4 for calling the FlavaMaskingGenerator obj's underlying member method code with example usage: obj._mask(mask, max_mask_patches) and returns: delta