code: def __call__(self):
      mask = np.zeros(shape=self.get_shape(), dtype=int)
      mask_count = 0
      while mask_count < self.total_mask_patches:
          max_mask_patches = self.total_mask_patches - mask_count
          max_mask_patches = min(
              max_mask_patches, self.mask_group_max_patches
          )
  
          delta = self._mask(mask, max_mask_patches)
          if delta == 0:
              break
          else:
              mask_count += delta
  
      return mask
  
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 5 for calling the FlavaMaskingGenerator obj's underlying member method code with example usage: obj.__call__() and returns: mask